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Ghanegolmohammadi F, Eslami M, Ohya Y. Systematic data analysis pipeline for quantitative morphological cell phenotyping. Comput Struct Biotechnol J 2024; 23:2949-2962. [PMID: 39104709 PMCID: PMC11298594 DOI: 10.1016/j.csbj.2024.07.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2024] [Revised: 07/09/2024] [Accepted: 07/10/2024] [Indexed: 08/07/2024] Open
Abstract
Quantitative morphological phenotyping (QMP) is an image-based method used to capture morphological features at both the cellular and population level. Its interdisciplinary nature, spanning from data collection to result analysis and interpretation, can lead to uncertainties, particularly among those new to this actively growing field. High analytical specificity for a typical QMP is achieved through sophisticated approaches that can leverage subtle cellular morphological changes. Here, we outline a systematic workflow to refine the QMP methodology. For a practical review, we describe the main steps of a typical QMP; in each step, we discuss the available methods, their applications, advantages, and disadvantages, along with the R functions and packages for easy implementation. This review does not cover theoretical backgrounds, but provides several references for interested researchers. It aims to broaden the horizons for future phenome studies and demonstrate how to exploit years of endeavors to achieve more with less.
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Affiliation(s)
- Farzan Ghanegolmohammadi
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
- Department of Integrated Biosciences, Graduate School of Frontier Sciences, The University of Tokyo, Kashiwa, Chiba, Japan
| | - Mohammad Eslami
- Harvard Ophthalmology AI Lab, Schepen’s Eye Research Institute of Massachusetts Eye and Ear Infirmary, Harvard Medical School, Boston, USA
| | - Yoshikazu Ohya
- Department of Integrated Biosciences, Graduate School of Frontier Sciences, The University of Tokyo, Kashiwa, Chiba, Japan
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2
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Cheng Y, Xu SM, Santucci K, Lindner G, Janitz M. Machine learning and related approaches in transcriptomics. Biochem Biophys Res Commun 2024; 724:150225. [PMID: 38852503 DOI: 10.1016/j.bbrc.2024.150225] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2024] [Revised: 05/18/2024] [Accepted: 06/03/2024] [Indexed: 06/11/2024]
Abstract
Data acquisition for transcriptomic studies used to be the bottleneck in the transcriptomic analytical pipeline. However, recent developments in transcriptome profiling technologies have increased researchers' ability to obtain data, resulting in a shift in focus to data analysis. Incorporating machine learning to traditional analytical methods allows the possibility of handling larger volumes of complex data more efficiently. Many bioinformaticians, especially those unfamiliar with ML in the study of human transcriptomics and complex biological systems, face a significant barrier stemming from their limited awareness of the current landscape of ML utilisation in this field. To address this gap, this review endeavours to introduce those individuals to the general types of ML, followed by a comprehensive range of more specific techniques, demonstrated through examples of their incorporation into analytical pipelines for human transcriptome investigations. Important computational aspects such as data pre-processing, task formulation, results (performance of ML models), and validation methods are encompassed. In hope of better practical relevance, there is a strong focus on studies published within the last five years, almost exclusively examining human transcriptomes, with outcomes compared with standard non-ML tools.
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Affiliation(s)
- Yuning Cheng
- School of Biotechnology and Biomolecular Sciences, University of New South Wales, Sydney, NSW, 2052, Australia
| | - Si-Mei Xu
- School of Biotechnology and Biomolecular Sciences, University of New South Wales, Sydney, NSW, 2052, Australia
| | - Kristina Santucci
- School of Biotechnology and Biomolecular Sciences, University of New South Wales, Sydney, NSW, 2052, Australia
| | - Grace Lindner
- School of Biotechnology and Biomolecular Sciences, University of New South Wales, Sydney, NSW, 2052, Australia
| | - Michael Janitz
- School of Biotechnology and Biomolecular Sciences, University of New South Wales, Sydney, NSW, 2052, Australia.
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3
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Chen H, Zhang B, Huang J. Recent advances and applications of artificial intelligence in 3D bioprinting. BIOPHYSICS REVIEWS 2024; 5:031301. [PMID: 39036708 PMCID: PMC11260195 DOI: 10.1063/5.0190208] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/04/2023] [Accepted: 06/11/2024] [Indexed: 07/23/2024]
Abstract
3D bioprinting techniques enable the precise deposition of living cells, biomaterials, and biomolecules, emerging as a promising approach for engineering functional tissues and organs. Meanwhile, recent advances in 3D bioprinting enable researchers to build in vitro models with finely controlled and complex micro-architecture for drug screening and disease modeling. Recently, artificial intelligence (AI) has been applied to different stages of 3D bioprinting, including medical image reconstruction, bioink selection, and printing process, with both classical AI and machine learning approaches. The ability of AI to handle complex datasets, make complex computations, learn from past experiences, and optimize processes dynamically makes it an invaluable tool in advancing 3D bioprinting. The review highlights the current integration of AI in 3D bioprinting and discusses future approaches to harness the synergistic capabilities of 3D bioprinting and AI for developing personalized tissues and organs.
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Affiliation(s)
| | - Bin Zhang
- Department of Mechanical and Aerospace Engineering, Brunel University London, London, United Kingdom
| | - Jie Huang
- Department of Mechanical Engineering, University College London, London, United Kingdom
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4
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Chi J, Shu J, Li M, Mudappathi R, Jin Y, Lewis F, Boon A, Qin X, Liu L, Gu H. Artificial Intelligence in Metabolomics: A Current Review. Trends Analyt Chem 2024; 178:117852. [PMID: 39071116 PMCID: PMC11271759 DOI: 10.1016/j.trac.2024.117852] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/30/2024]
Abstract
Metabolomics and artificial intelligence (AI) form a synergistic partnership. Metabolomics generates large datasets comprising hundreds to thousands of metabolites with complex relationships. AI, aiming to mimic human intelligence through computational modeling, possesses extraordinary capabilities for big data analysis. In this review, we provide a recent overview of the methodologies and applications of AI in metabolomics studies in the context of systems biology and human health. We first introduce the AI concept, history, and key algorithms for machine learning and deep learning, summarizing their strengths and weaknesses. We then discuss studies that have successfully used AI across different aspects of metabolomic analysis, including analytical detection, data preprocessing, biomarker discovery, predictive modeling, and multi-omics data integration. Lastly, we discuss the existing challenges and future perspectives in this rapidly evolving field. Despite limitations and challenges, the combination of metabolomics and AI holds great promises for revolutionary advancements in enhancing human health.
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Affiliation(s)
- Jinhua Chi
- College of Health Solutions, Arizona State University, Phoenix, AZ 85004, USA
- Center for Translational Science, Florida International University, Port St. Lucie, FL 34987, USA
| | - Jingmin Shu
- College of Health Solutions, Arizona State University, Phoenix, AZ 85004, USA
- Center for Personalized Diagnostics, Biodesign Institute, Arizona State University, Tempe, AZ 85281, USA
| | - Ming Li
- Phoenix VA Health Care System, Phoenix, AZ 85012, USA
- University of Arizona College of Medicine, Phoenix, AZ 85004, USA
| | - Rekha Mudappathi
- College of Health Solutions, Arizona State University, Phoenix, AZ 85004, USA
- Center for Personalized Diagnostics, Biodesign Institute, Arizona State University, Tempe, AZ 85281, USA
| | - Yan Jin
- Center for Translational Science, Florida International University, Port St. Lucie, FL 34987, USA
| | - Freeman Lewis
- Center for Translational Science, Florida International University, Port St. Lucie, FL 34987, USA
| | - Alexandria Boon
- Center for Translational Science, Florida International University, Port St. Lucie, FL 34987, USA
| | - Xiaoyan Qin
- College of Liberal Arts and Sciences, Arizona State University, Tempe, AZ 85281, USA
| | - Li Liu
- College of Health Solutions, Arizona State University, Phoenix, AZ 85004, USA
- Center for Personalized Diagnostics, Biodesign Institute, Arizona State University, Tempe, AZ 85281, USA
| | - Haiwei Gu
- College of Health Solutions, Arizona State University, Phoenix, AZ 85004, USA
- Center for Translational Science, Florida International University, Port St. Lucie, FL 34987, USA
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5
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Qin XY, Shirakami Y, Honda M, Yeh SH, Numata K, Lai YY, Li CL, Wei F, Xu Y, Imai K, Takai K, Chuma M, Komatsu N, Furutani Y, Gailhouste L, Aikata H, Chayama K, Enomoto M, Tateishi R, Kawaguchi K, Yamashita T, Kaneko S, Nagaoka K, Tanaka M, Sasaki Y, Tanaka Y, Baba H, Miura K, Ochi S, Masaki T, Kojima S, Matsuura T, Shimizu M, Chen PJ, Moriwaki H, Suzuki H. Serum MYCN as a predictive biomarker of prognosis and therapeutic response in the prevention of hepatocellular carcinoma recurrence. Int J Cancer 2024; 155:582-594. [PMID: 38380807 DOI: 10.1002/ijc.34893] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2023] [Revised: 01/22/2024] [Accepted: 01/31/2024] [Indexed: 02/22/2024]
Abstract
The proto-oncogene MYCN expression marked a cancer stem-like cell population in hepatocellular carcinoma (HCC) and served as a therapeutic target of acyclic retinoid (ACR), an orally administered vitamin A derivative that has demonstrated promising efficacy and safety in reducing HCC recurrence. This study investigated the role of MYCN as a predictive biomarker for therapeutic response to ACR and prognosis of HCC. MYCN gene expression in HCC was analyzed in the Cancer Genome Atlas and a Taiwanese cohort (N = 118). Serum MYCN protein levels were assessed in healthy controls (N = 15), patients with HCC (N = 116), pre- and post-surgical patients with HCC (N = 20), and a subset of patients from a phase 3 clinical trial of ACR (N = 68, NCT01640808). The results showed increased MYCN gene expression in HCC tumors, which positively correlated with HCC recurrence in non-cirrhotic or single-tumor patients. Serum MYCN protein levels were higher in patients with HCC, decreased after surgical resection of HCC, and were associated with liver functional reserve and fibrosis markers, as well as long-term HCC prognosis (>4 years). Subgroup analysis of a phase 3 clinical trial of ACR identified serum MYCN as the risk factor most strongly associated with HCC recurrence. Patients with HCC with higher serum MYCN levels after a 4-week treatment of ACR exhibited a significantly higher risk of recurrence (hazard ratio 3.27; p = .022). In conclusion, serum MYCN holds promise for biomarker-based precision medicine for the prevention of HCC, long-term prognosis of early-stage HCC, and identification of high-response subgroups for ACR-based treatment.
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Affiliation(s)
- Xian-Yang Qin
- Laboratory for Cellular Function Conversion Technology, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
- Liver Cancer Prevention Research Unit, RIKEN Cluster for Pioneering Research, Saitama, Japan
| | - Yohei Shirakami
- Department of Gastroenterology, Graduate School of Medicine, Gifu University, Gifu, Japan
| | - Masao Honda
- Department of Gastroenterology, Graduate School of Medical Sciences, Kanazawa University, Kanazawa, Japan
| | - Shiou-Hwei Yeh
- Department of Microbiology, National Taiwan University College of Medicine, Taipei, Taiwan
| | - Kazushi Numata
- Gastroenterological Center, Yokohama City University Medical Center, Yokohama, Japan
| | - Ya-Yun Lai
- Department of Microbiology, National Taiwan University College of Medicine, Taipei, Taiwan
| | - Chiao-Ling Li
- Department of Microbiology, National Taiwan University College of Medicine, Taipei, Taiwan
| | - Feifei Wei
- Division of Cancer Immunotherapy, Kanagawa Cancer Center Research Institute, Yokohama, Japan
| | - Yali Xu
- Laboratory for Cellular Function Conversion Technology, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | - Kenji Imai
- Department of Gastroenterology, Graduate School of Medicine, Gifu University, Gifu, Japan
| | - Koji Takai
- Department of Gastroenterology, Graduate School of Medicine, Gifu University, Gifu, Japan
| | - Makoto Chuma
- Gastroenterological Center, Yokohama City University Medical Center, Yokohama, Japan
| | - Nagisa Komatsu
- Gastroenterological Center, Yokohama City University Medical Center, Yokohama, Japan
| | - Yutaka Furutani
- Liver Cancer Prevention Research Unit, RIKEN Cluster for Pioneering Research, Saitama, Japan
- Department of Laboratory Medicine, The Jikei University School of Medicine, Tokyo, Japan
| | - Luc Gailhouste
- Liver Cancer Prevention Research Unit, RIKEN Cluster for Pioneering Research, Saitama, Japan
- Laboratory for Brain Development and Disorders, RIKEN Center for Brain Science, Saitama, Japan
| | | | - Kazuaki Chayama
- Collaborative Research Laboratory of Medical Innovation, Hiroshima University, Hiroshima, Japan
- Hiroshima Institute of Life Sciences, Hiroshima, Japan
- RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | - Masaru Enomoto
- Department of Hepatology, Osaka City University Graduate School of Medicine, Osaka, Japan
| | - Ryosuke Tateishi
- Department of Gastroenterology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Kazunori Kawaguchi
- Department of Gastroenterology, Graduate School of Medical Sciences, Kanazawa University, Kanazawa, Japan
| | - Tatsuya Yamashita
- Department of Gastroenterology, Graduate School of Medical Sciences, Kanazawa University, Kanazawa, Japan
| | - Shuichi Kaneko
- Department of Gastroenterology, Graduate School of Medical Sciences, Kanazawa University, Kanazawa, Japan
| | - Katsuya Nagaoka
- Department of Gastroenterology and Hepatology, Graduate School of Medical Sciences, Kumamoto University, Kumamoto, Japan
| | - Motohiko Tanaka
- Department of Gastroenterology and Hepatology, Graduate School of Medical Sciences, Kumamoto University, Kumamoto, Japan
- Public Health and Welfare Bureau, City of Kumamoto, Kumamoto, Japan
| | - Yutaka Sasaki
- Department of Gastroenterology and Hepatology, Graduate School of Medical Sciences, Kumamoto University, Kumamoto, Japan
- Department of Gastroenterology, Osaka Central Hospital, Osaka, Japan
| | - Yasuhito Tanaka
- Department of Gastroenterology and Hepatology, Graduate School of Medical Sciences, Kumamoto University, Kumamoto, Japan
| | - Hideo Baba
- Department of Gastroenterological Surgery, Graduate School of Medical Sciences, Kumamoto University, Kumamoto, Japan
| | - Kouichi Miura
- Division of Gastroenterology, Jichi Medical University School of Medicine, Tochigi, Japan
| | - Sae Ochi
- Department of Laboratory Medicine, The Jikei University School of Medicine, Tokyo, Japan
| | - Takahiro Masaki
- Department of Laboratory Medicine, The Jikei University School of Medicine, Tokyo, Japan
| | - Soichi Kojima
- Liver Cancer Prevention Research Unit, RIKEN Cluster for Pioneering Research, Saitama, Japan
| | - Tomokazu Matsuura
- Liver Cancer Prevention Research Unit, RIKEN Cluster for Pioneering Research, Saitama, Japan
- Department of Laboratory Medicine, The Jikei University School of Medicine, Tokyo, Japan
| | - Masahito Shimizu
- Department of Gastroenterology, Graduate School of Medicine, Gifu University, Gifu, Japan
| | - Pei-Jer Chen
- Graduate Institute of Clinical Medicine, Department of Internal Medicine, National Taiwan University College of Medicine and Hospital, Taipei, Taiwan
| | - Hisataka Moriwaki
- Department of Gastroenterology, Graduate School of Medicine, Gifu University, Gifu, Japan
| | - Harukazu Suzuki
- Laboratory for Cellular Function Conversion Technology, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
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6
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Soyer SM, Ozbek P, Kasavi C. Lung Adenocarcinoma Systems Biomarker and Drug Candidates Identified by Machine Learning, Gene Expression Data, and Integrative Bioinformatics Pipeline. OMICS : A JOURNAL OF INTEGRATIVE BIOLOGY 2024; 28:408-420. [PMID: 38979602 DOI: 10.1089/omi.2024.0121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/10/2024]
Abstract
Lung adenocarcinoma (LUAD) is a significant planetary health challenge with its high morbidity and mortality rate, not to mention the marked interindividual variability in treatment outcomes and side effects. There is an urgent need for robust systems biomarkers that can help with early cancer diagnosis, prediction of treatment outcomes, and design of precision/personalized medicines for LUAD. The present study aimed at systems biomarkers of LUAD and deployed integrative bioinformatics and machine learning tools to harness gene expression data. Predictive models were developed to stratify patients based on prognostic outcomes. Importantly, we report here several potential key genes, for example, PMEL and BRIP1, and pathways implicated in the progression and prognosis of LUAD that could potentially be targeted for precision/personalized medicine in the future. Our drug repurposing analysis and molecular docking simulations suggested eight drug candidates for LUAD such as heat shock protein 90 inhibitors, cardiac glycosides, an antipsychotic agent (trifluoperazine), and a calcium ionophore (ionomycin). In summary, this study identifies several promising leads on systems biomarkers and drug candidates for LUAD. The findings also attest to the importance of integrative bioinformatics, structural biology and machine learning techniques in biomarker discovery, and precision oncology research and development.
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Affiliation(s)
- Semra Melis Soyer
- Department of Bioengineering, Faculty of Engineering, Marmara University, İstanbul, Türkiye
| | - Pemra Ozbek
- Department of Bioengineering, Faculty of Engineering, Marmara University, İstanbul, Türkiye
| | - Ceyda Kasavi
- Department of Bioengineering, Faculty of Engineering, Marmara University, İstanbul, Türkiye
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7
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Tahıl G, Delorme F, Le Berre D, Monflier É, Sayede A, Tilloy S. Stereoisomers Are Not Machine Learning's Best Friends. J Chem Inf Model 2024; 64:5451-5469. [PMID: 38949069 DOI: 10.1021/acs.jcim.4c00318] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/02/2024]
Abstract
This study addresses the challenge of accurately identifying stereoisomers in cheminformatics, which originates from our objective to apply machine learning to predict the association constant between cyclodextrin and a guest. Identifying stereoisomers is indeed crucial for machine learning applications. Current tools offer various molecular descriptors, including their textual representation as Isomeric SMILES that can distinguish stereoisomers. However, such representation is text-based and does not have a fixed size, so a conversion is needed to make it usable to machine learning approaches. Word embedding techniques can be used to solve this problem. Mol2vec, a word embedding approach for molecules, offers such a conversion. Unfortunately, it cannot distinguish between stereoisomers due to its inability to capture the spatial configuration of molecular structures. This study proposes several approaches that use word embedding techniques to handle molecular discrimination using stereochemical information on molecules or considering Isomeric SMILES notation as a text in Natural Language Processing. Our aim is to generate a distinct vector for each unique molecule, correctly identifying stereoisomer information in cheminformatics. The proposed approaches are then compared to our original machine learning task: predicting the association constant between cyclodextrin and a guest molecule.
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Affiliation(s)
- Gökhan Tahıl
- Centre de Recherche en Informatique de Lens (CRIL)Univ. Artois, CNRS, Centre de Recherche en Informatique de Lens (CRIL), F-62300 Lens, France
- Univ. Artois, CNRS, Centrale Lille, Univ. Lille, UMR 8181, Unité de Catalyse et Chimie du Solide (UCCS), rue Jean Souvraz, SP 18, F-62307 Lens Cedex, France
| | - Fabien Delorme
- Centre de Recherche en Informatique de Lens (CRIL)Univ. Artois, CNRS, Centre de Recherche en Informatique de Lens (CRIL), F-62300 Lens, France
| | - Daniel Le Berre
- Centre de Recherche en Informatique de Lens (CRIL)Univ. Artois, CNRS, Centre de Recherche en Informatique de Lens (CRIL), F-62300 Lens, France
| | - Éric Monflier
- Univ. Artois, CNRS, Centrale Lille, Univ. Lille, UMR 8181, Unité de Catalyse et Chimie du Solide (UCCS), rue Jean Souvraz, SP 18, F-62307 Lens Cedex, France
| | - Adlane Sayede
- Univ. Artois, CNRS, Centrale Lille, Univ. Lille, UMR 8181, Unité de Catalyse et Chimie du Solide (UCCS), rue Jean Souvraz, SP 18, F-62307 Lens Cedex, France
| | - Sébastien Tilloy
- Univ. Artois, CNRS, Centrale Lille, Univ. Lille, UMR 8181, Unité de Catalyse et Chimie du Solide (UCCS), rue Jean Souvraz, SP 18, F-62307 Lens Cedex, France
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8
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Li H, Han Z, Sun Y, Wang F, Hu P, Gao Y, Bai X, Peng S, Ren C, Xu X, Liu Z, Chen H, Yang Y, Bo X. CGMega: explainable graph neural network framework with attention mechanisms for cancer gene module dissection. Nat Commun 2024; 15:5997. [PMID: 39013885 PMCID: PMC11252405 DOI: 10.1038/s41467-024-50426-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2023] [Accepted: 07/09/2024] [Indexed: 07/18/2024] Open
Abstract
Cancer is rarely the straightforward consequence of an abnormality in a single gene, but rather reflects a complex interplay of many genes, represented as gene modules. Here, we leverage the recent advances of model-agnostic interpretation approach and develop CGMega, an explainable and graph attention-based deep learning framework to perform cancer gene module dissection. CGMega outperforms current approaches in cancer gene prediction, and it provides a promising approach to integrate multi-omics information. We apply CGMega to breast cancer cell line and acute myeloid leukemia (AML) patients, and we uncover the high-order gene module formed by ErbB family and tumor factors NRG1, PPM1A and DLG2. We identify 396 candidate AML genes, and observe the enrichment of either known AML genes or candidate AML genes in a single gene module. We also identify patient-specific AML genes and associated gene modules. Together, these results indicate that CGMega can be used to dissect cancer gene modules, and provide high-order mechanistic insights into cancer development and heterogeneity.
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Affiliation(s)
- Hao Li
- Academy of Military Medical Sciences, Beijing, China
| | - Zebei Han
- Department of Computer Science and Engineering, Shanghai Jiao Tong University, Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering, Shanghai, China
| | - Yu Sun
- Academy of Military Medical Sciences, Beijing, China
| | - Fu Wang
- Department of Computer Science and Engineering, Shanghai Jiao Tong University, Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering, Shanghai, China
| | - Pengzhen Hu
- School of Life Sciences, Northwestern Polytechnical University, Xi'an, China
| | - Yuang Gao
- Department of Hematology, PLA General Hospital, the Fifth Medical Center, Beijing, China
| | - Xuemei Bai
- Academy of Military Medical Sciences, Beijing, China
| | - Shiyu Peng
- Academy of Military Medical Sciences, Beijing, China
| | - Chao Ren
- Academy of Military Medical Sciences, Beijing, China
| | - Xiang Xu
- Academy of Military Medical Sciences, Beijing, China
| | - Zeyu Liu
- Academy of Military Medical Sciences, Beijing, China
| | - Hebing Chen
- Academy of Military Medical Sciences, Beijing, China.
| | - Yang Yang
- Department of Computer Science and Engineering, Shanghai Jiao Tong University, Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering, Shanghai, China.
| | - Xiaochen Bo
- Academy of Military Medical Sciences, Beijing, China.
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9
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Llitjos JF, Carrol ED, Osuchowski MF, Bonneville M, Scicluna BP, Payen D, Randolph AG, Witte S, Rodriguez-Manzano J, François B. Enhancing sepsis biomarker development: key considerations from public and private perspectives. Crit Care 2024; 28:238. [PMID: 39003476 PMCID: PMC11246589 DOI: 10.1186/s13054-024-05032-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2024] [Accepted: 07/10/2024] [Indexed: 07/15/2024] Open
Abstract
Implementation of biomarkers in sepsis and septic shock in emergency situations, remains highly challenging. This viewpoint arose from a public-private 3-day workshop aiming to facilitate the transition of sepsis biomarkers into clinical practice. The authors consist of international academic researchers and clinician-scientists and industry experts who gathered (i) to identify current obstacles impeding biomarker research in sepsis, (ii) to outline the important milestones of the critical path of biomarker development and (iii) to discuss novel avenues in biomarker discovery and implementation. To define more appropriately the potential place of biomarkers in sepsis, a better understanding of sepsis pathophysiology is mandatory, in particular the sepsis patient's trajectory from the early inflammatory onset to the late persisting immunosuppression phase. This time-varying host response urges to develop time-resolved test to characterize persistence of immunological dysfunctions. Furthermore, age-related difference has to be considered between adult and paediatric septic patients. In this context, numerous barriers to biomarker adoption in practice, such as lack of consensus about diagnostic performances, the absence of strict recommendations for sepsis biomarker development, cost and resources implications, methodological validation challenges or limited awareness and education have been identified. Biomarker-guided interventions for sepsis to identify patients that would benefit more from therapy, such as sTREM-1-guided Nangibotide treatment or Adrenomedullin-guided Enibarcimab treatment, appear promising but require further evaluation. Artificial intelligence also has great potential in the sepsis biomarker discovery field through capability to analyse high volume complex data and identify complex multiparametric patient endotypes or trajectories. To conclude, biomarker development in sepsis requires (i) a comprehensive and multidisciplinary approach employing the most advanced analytical tools, (ii) the creation of a platform that collaboratively merges scientific and commercial needs and (iii) the support of an expedited regulatory approval process.
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Affiliation(s)
- Jean-Francois Llitjos
- Open Innovation and Partnerships (OI&P), bioMérieux S.A., Marcy l'Etoile, France.
- Anesthesiology and Critical Care Medicine, Hospices Civils de Lyon, Edouard Herriot Hospital, Lyon, France.
| | - Enitan D Carrol
- Department of Clinical Infection, Microbiology and Immunology, University of Liverpool Institute of Infection Veterinary and Ecological Sciences, Liverpool, UK
- Department of Paediatric Infectious Diseases and Immunology, Alder Hey Children's NHS Foundation Trust, Liverpool, UK
| | - Marcin F Osuchowski
- Ludwig Boltzmann Institute for Traumatology, The Research Center in Cooperation with AUVA, Vienna, Austria
| | - Marc Bonneville
- Medical and Scientific Affairs, Institut Mérieux, Lyon, France
| | - Brendon P Scicluna
- Department of Applied Biomedical Science, Faculty of Health Sciences, Mater Dei Hospital, University of Malta, Msida, Malta
- Centre for Molecular Medicine and Biobanking, University of Malta, Msida, Malta
| | - Didier Payen
- Paris 7 University Denis Diderot, Paris Sorbonne, Cité, France
| | - Adrienne G Randolph
- Departments of Anaesthesia and Pediatrics, Harvard Medical School, Boston, MA, USA
- Department of Anesthesiology, Critical Care and Pain Medicine, Boston Children's Hospital, Boston, MA, USA
| | | | | | - Bruno François
- Medical-Surgical Intensive Care Unit, Réanimation Polyvalente, Dupuytren University Hospital, CHU de Limoges, 2 Avenue Martin Luther King, 87042, Limoges Cedex, France.
- Inserm CIC 1435, Dupuytren University Hospital, Limoges, France.
- Inserm UMR 1092, Medicine Faculty, University of Limoges, Limoges, France.
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10
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Mohr AE, Ortega-Santos CP, Whisner CM, Klein-Seetharaman J, Jasbi P. Navigating Challenges and Opportunities in Multi-Omics Integration for Personalized Healthcare. Biomedicines 2024; 12:1496. [PMID: 39062068 PMCID: PMC11274472 DOI: 10.3390/biomedicines12071496] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2024] [Revised: 06/25/2024] [Accepted: 06/28/2024] [Indexed: 07/28/2024] Open
Abstract
The field of multi-omics has witnessed unprecedented growth, converging multiple scientific disciplines and technological advances. This surge is evidenced by a more than doubling in multi-omics scientific publications within just two years (2022-2023) since its first referenced mention in 2002, as indexed by the National Library of Medicine. This emerging field has demonstrated its capability to provide comprehensive insights into complex biological systems, representing a transformative force in health diagnostics and therapeutic strategies. However, several challenges are evident when merging varied omics data sets and methodologies, interpreting vast data dimensions, streamlining longitudinal sampling and analysis, and addressing the ethical implications of managing sensitive health information. This review evaluates these challenges while spotlighting pivotal milestones: the development of targeted sampling methods, the use of artificial intelligence in formulating health indices, the integration of sophisticated n-of-1 statistical models such as digital twins, and the incorporation of blockchain technology for heightened data security. For multi-omics to truly revolutionize healthcare, it demands rigorous validation, tangible real-world applications, and smooth integration into existing healthcare infrastructures. It is imperative to address ethical dilemmas, paving the way for the realization of a future steered by omics-informed personalized medicine.
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Affiliation(s)
- Alex E. Mohr
- Systems Precision Engineering and Advanced Research (SPEAR), Theriome Inc., Phoenix, AZ 85004, USA; (A.E.M.); (C.P.O.-S.); (C.M.W.); (J.K.-S.)
- College of Health Solutions, Arizona State University, Phoenix, AZ 85004, USA
- Biodesign Institute Center for Health Through Microbiomes, Arizona State University, Tempe, AZ 85281, USA
| | - Carmen P. Ortega-Santos
- Systems Precision Engineering and Advanced Research (SPEAR), Theriome Inc., Phoenix, AZ 85004, USA; (A.E.M.); (C.P.O.-S.); (C.M.W.); (J.K.-S.)
- Department of Exercise and Nutrition Sciences, Milken Institute School of Public Health, George Washington University, Washington, DC 20052, USA
| | - Corrie M. Whisner
- Systems Precision Engineering and Advanced Research (SPEAR), Theriome Inc., Phoenix, AZ 85004, USA; (A.E.M.); (C.P.O.-S.); (C.M.W.); (J.K.-S.)
- College of Health Solutions, Arizona State University, Phoenix, AZ 85004, USA
- Biodesign Institute Center for Health Through Microbiomes, Arizona State University, Tempe, AZ 85281, USA
| | - Judith Klein-Seetharaman
- Systems Precision Engineering and Advanced Research (SPEAR), Theriome Inc., Phoenix, AZ 85004, USA; (A.E.M.); (C.P.O.-S.); (C.M.W.); (J.K.-S.)
- College of Health Solutions, Arizona State University, Phoenix, AZ 85004, USA
- School of Molecular Sciences, Arizona State University, Tempe, AZ 85281, USA
| | - Paniz Jasbi
- Systems Precision Engineering and Advanced Research (SPEAR), Theriome Inc., Phoenix, AZ 85004, USA; (A.E.M.); (C.P.O.-S.); (C.M.W.); (J.K.-S.)
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11
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Xu C, Alameri A, Leong W, Johnson E, Chen Z, Xu B, Leong KW. Multiscale engineering of brain organoids for disease modeling. Adv Drug Deliv Rev 2024; 210:115344. [PMID: 38810702 PMCID: PMC11265575 DOI: 10.1016/j.addr.2024.115344] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2024] [Revised: 04/23/2024] [Accepted: 05/25/2024] [Indexed: 05/31/2024]
Abstract
Brain organoids hold great potential for modeling human brain development and pathogenesis. They recapitulate certain aspects of the transcriptional trajectory, cellular diversity, tissue architecture and functions of the developing brain. In this review, we explore the engineering strategies to control the molecular-, cellular- and tissue-level inputs to achieve high-fidelity brain organoids. We review the application of brain organoids in neural disorder modeling and emerging bioengineering methods to improve data collection and feature extraction at multiscale. The integration of multiscale engineering strategies and analytical methods has significant potential to advance insight into neurological disorders and accelerate drug development.
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Affiliation(s)
- Cong Xu
- Department of Biomedical Engineering, Columbia University, New York, NY 10027, USA
| | - Alia Alameri
- Department of Biomedical Engineering, Columbia University, New York, NY 10027, USA
| | - Wei Leong
- Department of Biomedical Engineering, Columbia University, New York, NY 10027, USA
| | - Emily Johnson
- Department of Biomedical Engineering, Columbia University, New York, NY 10027, USA
| | - Zaozao Chen
- Department of Biomedical Engineering, Columbia University, New York, NY 10027, USA
| | - Bin Xu
- Department of Psychiatry, Columbia University, New York, NY 10032, USA.
| | - Kam W Leong
- Department of Biomedical Engineering, Columbia University, New York, NY 10027, USA.
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12
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Christodoulou A, Katsarou MS, Emmanouil C, Gavrielatos M, Georgiou D, Tsolakou A, Papasavva M, Economou V, Nanou V, Nikolopoulos I, Daganou M, Argyraki A, Stefanidis E, Metaxas G, Panagiotou E, Michalopoulos I, Drakoulis N. A Machine Learning-Based Web Tool for the Severity Prediction of COVID-19. BIOTECH 2024; 13:22. [PMID: 39051337 PMCID: PMC11270362 DOI: 10.3390/biotech13030022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2023] [Revised: 06/13/2024] [Accepted: 06/20/2024] [Indexed: 07/27/2024] Open
Abstract
Predictive tools provide a unique opportunity to explain the observed differences in outcome between patients of the COVID-19 pandemic. The aim of this study was to associate individual demographic and clinical characteristics with disease severity in COVID-19 patients and to highlight the importance of machine learning (ML) in disease prognosis. The study enrolled 344 unvaccinated patients with confirmed SARS-CoV-2 infection. Data collected by integrating questionnaires and medical records were imported into various classification machine learning algorithms, and the algorithm and the hyperparameters with the greatest predictive ability were selected for use in a disease outcome prediction web tool. Of 111 independent features, age, sex, hypertension, obesity, and cancer comorbidity were found to be associated with severe COVID-19. Our prognostic tool can contribute to a successful therapeutic approach via personalized treatment. Although at the present time vaccination is not considered mandatory, this algorithm could encourage vulnerable groups to be vaccinated.
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Affiliation(s)
- Avgi Christodoulou
- Research Group of Clinical Pharmacology and Pharmacogenomics Faculty of Pharmacy, School oh Health Sciences, National and Kapodistrian University of Athens, 15771 Athens, Greece; (A.C.); (M.-S.K.); (A.T.); (V.E.); (N.D.)
- Sotiria Thoracic Diseases Hospital of Athens, 11527 Athens, Greece; (V.N.); (I.N.); (M.D.); (A.A.); (E.S.); (G.M.); (E.P.)
| | - Martha-Spyridoula Katsarou
- Research Group of Clinical Pharmacology and Pharmacogenomics Faculty of Pharmacy, School oh Health Sciences, National and Kapodistrian University of Athens, 15771 Athens, Greece; (A.C.); (M.-S.K.); (A.T.); (V.E.); (N.D.)
| | - Christina Emmanouil
- Centre of Systems Biology, Biomedical Research Foundation, Academy of Athens, 11527 Athens, Greece; (C.E.); (M.G.); (D.G.)
- Department of Biology, National and Kapodistrian University of Athens, 15772 Athens, Greece
- Institute for Bioinnovation, Biomedical Sciences Research Center ‘Alexander Fleming’, 16672 Vari, Greece
| | - Marios Gavrielatos
- Centre of Systems Biology, Biomedical Research Foundation, Academy of Athens, 11527 Athens, Greece; (C.E.); (M.G.); (D.G.)
- Department of Informatics and Telecommunications, National and Kapodistrian University of Athens, 16122 Athens, Greece
- Department of Neuroscience, Mayo Clinic, Jacksonville, FL 32224, USA
| | - Dimitrios Georgiou
- Centre of Systems Biology, Biomedical Research Foundation, Academy of Athens, 11527 Athens, Greece; (C.E.); (M.G.); (D.G.)
- School of Electrical and Computer Engineering, National and Technical University of Athens, 15773 Athens, Greece
| | - Annia Tsolakou
- Research Group of Clinical Pharmacology and Pharmacogenomics Faculty of Pharmacy, School oh Health Sciences, National and Kapodistrian University of Athens, 15771 Athens, Greece; (A.C.); (M.-S.K.); (A.T.); (V.E.); (N.D.)
| | - Maria Papasavva
- Department of Pharmacy, School of Health Sciences, Frederick University, 1036 Nicosia, Cyprus;
| | - Vasiliki Economou
- Research Group of Clinical Pharmacology and Pharmacogenomics Faculty of Pharmacy, School oh Health Sciences, National and Kapodistrian University of Athens, 15771 Athens, Greece; (A.C.); (M.-S.K.); (A.T.); (V.E.); (N.D.)
| | - Vasiliki Nanou
- Sotiria Thoracic Diseases Hospital of Athens, 11527 Athens, Greece; (V.N.); (I.N.); (M.D.); (A.A.); (E.S.); (G.M.); (E.P.)
| | - Ioannis Nikolopoulos
- Sotiria Thoracic Diseases Hospital of Athens, 11527 Athens, Greece; (V.N.); (I.N.); (M.D.); (A.A.); (E.S.); (G.M.); (E.P.)
| | - Maria Daganou
- Sotiria Thoracic Diseases Hospital of Athens, 11527 Athens, Greece; (V.N.); (I.N.); (M.D.); (A.A.); (E.S.); (G.M.); (E.P.)
| | - Aikaterini Argyraki
- Sotiria Thoracic Diseases Hospital of Athens, 11527 Athens, Greece; (V.N.); (I.N.); (M.D.); (A.A.); (E.S.); (G.M.); (E.P.)
| | - Evaggelos Stefanidis
- Sotiria Thoracic Diseases Hospital of Athens, 11527 Athens, Greece; (V.N.); (I.N.); (M.D.); (A.A.); (E.S.); (G.M.); (E.P.)
| | - Gerasimos Metaxas
- Sotiria Thoracic Diseases Hospital of Athens, 11527 Athens, Greece; (V.N.); (I.N.); (M.D.); (A.A.); (E.S.); (G.M.); (E.P.)
| | - Emmanouil Panagiotou
- Sotiria Thoracic Diseases Hospital of Athens, 11527 Athens, Greece; (V.N.); (I.N.); (M.D.); (A.A.); (E.S.); (G.M.); (E.P.)
| | - Ioannis Michalopoulos
- Centre of Systems Biology, Biomedical Research Foundation, Academy of Athens, 11527 Athens, Greece; (C.E.); (M.G.); (D.G.)
| | - Nikolaos Drakoulis
- Research Group of Clinical Pharmacology and Pharmacogenomics Faculty of Pharmacy, School oh Health Sciences, National and Kapodistrian University of Athens, 15771 Athens, Greece; (A.C.); (M.-S.K.); (A.T.); (V.E.); (N.D.)
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13
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Zhou X, Cai F, Li S, Li G, Zhang C, Xie J, Yang Y. Machine learning techniques for prediction in pregnancy complicated by autoimmune rheumatic diseases: Applications and challenges. Int Immunopharmacol 2024; 134:112238. [PMID: 38735259 DOI: 10.1016/j.intimp.2024.112238] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2024] [Revised: 05/01/2024] [Accepted: 05/08/2024] [Indexed: 05/14/2024]
Abstract
Autoimmune rheumatic diseases are chronic conditions affecting multiple systems and often occurring in young women of childbearing age. The diseases and the physiological characteristics of pregnancy significantly impact maternal-fetal health and pregnancy outcomes. Currently, the integration of big data with healthcare has led to the increasing popularity of using machine learning (ML) to mine clinical data for studying pregnancy complications. In this review, we introduce the basics of ML and the recent advances and trends of ML in different prediction applications for common pregnancy complications by autoimmune rheumatic diseases. Finally, the challenges and future for enhancing the accuracy, reliability, and clinical applicability of ML in prediction have been discussed. This review will provide insights into the utilization of ML in identifying and assisting clinical decision-making for pregnancy complications, while also establishing a foundation for exploring comprehensive management strategies for pregnancy and enhancing maternal and child health.
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Affiliation(s)
- Xiaoshi Zhou
- Department of Pharmacy, Sichuan Academy of Medical Sciences & Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Feifei Cai
- Department of Pharmacy, Sichuan Academy of Medical Sciences & Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Shiran Li
- Department of Pharmacy, Sichuan Academy of Medical Sciences & Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Guolin Li
- Department of Pharmacy, Sichuan Academy of Medical Sciences & Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China; School of Basic Medicine and Clinical Pharmacy, China Pharmaceutical University, Nanjing, China
| | - Changji Zhang
- Department of Pharmacy, Sichuan Academy of Medical Sciences & Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China; School of Basic Medicine and Clinical Pharmacy, China Pharmaceutical University, Nanjing, China
| | - Jingxian Xie
- Department of Pharmacy, Sichuan Academy of Medical Sciences & Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China; College of Pharmacy, Southwest Medical University, Luzhou, China
| | - Yong Yang
- Department of Pharmacy, Sichuan Academy of Medical Sciences & Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China.
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Shi Y, Zhou M, Chang C, Jiang P, Wei K, Zhao J, Shan Y, Zheng Y, Zhao F, Lv X, Guo S, Wang F, He D. Advancing precision rheumatology: applications of machine learning for rheumatoid arthritis management. Front Immunol 2024; 15:1409555. [PMID: 38915408 PMCID: PMC11194317 DOI: 10.3389/fimmu.2024.1409555] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2024] [Accepted: 05/24/2024] [Indexed: 06/26/2024] Open
Abstract
Rheumatoid arthritis (RA) is an autoimmune disease causing progressive joint damage. Early diagnosis and treatment is critical, but remains challenging due to RA complexity and heterogeneity. Machine learning (ML) techniques may enhance RA management by identifying patterns within multidimensional biomedical data to improve classification, diagnosis, and treatment predictions. In this review, we summarize the applications of ML for RA management. Emerging studies or applications have developed diagnostic and predictive models for RA that utilize a variety of data modalities, including electronic health records, imaging, and multi-omics data. High-performance supervised learning models have demonstrated an Area Under the Curve (AUC) exceeding 0.85, which is used for identifying RA patients and predicting treatment responses. Unsupervised learning has revealed potential RA subtypes. Ongoing research is integrating multimodal data with deep learning to further improve performance. However, key challenges remain regarding model overfitting, generalizability, validation in clinical settings, and interpretability. Small sample sizes and lack of diverse population testing risks overestimating model performance. Prospective studies evaluating real-world clinical utility are lacking. Enhancing model interpretability is critical for clinician acceptance. In summary, while ML shows promise for transforming RA management through earlier diagnosis and optimized treatment, larger scale multisite data, prospective clinical validation of interpretable models, and testing across diverse populations is still needed. As these gaps are addressed, ML may pave the way towards precision medicine in RA.
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Affiliation(s)
- Yiming Shi
- Department of Rheumatology, Shanghai Guanghua Hospital of Integrative Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- Guanghua Clinical Medical College, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- Institute of Arthritis Research in Integrative Medicine, Shanghai Academy of Traditional Chinese Medicine, Shanghai, China
| | - Mi Zhou
- Department of Rheumatology, Shanghai Guanghua Hospital of Integrative Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- Institute of Arthritis Research in Integrative Medicine, Shanghai Academy of Traditional Chinese Medicine, Shanghai, China
| | - Cen Chang
- Department of Rheumatology, Shanghai Guanghua Hospital of Integrative Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- Institute of Arthritis Research in Integrative Medicine, Shanghai Academy of Traditional Chinese Medicine, Shanghai, China
| | - Ping Jiang
- Department of Rheumatology, Shanghai Guanghua Hospital of Integrative Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- Guanghua Clinical Medical College, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- Institute of Arthritis Research in Integrative Medicine, Shanghai Academy of Traditional Chinese Medicine, Shanghai, China
| | - Kai Wei
- Department of Rheumatology, Shanghai Guanghua Hospital of Integrative Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- Guanghua Clinical Medical College, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- Institute of Arthritis Research in Integrative Medicine, Shanghai Academy of Traditional Chinese Medicine, Shanghai, China
| | - Jianan Zhao
- Department of Rheumatology, Shanghai Guanghua Hospital of Integrative Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- Guanghua Clinical Medical College, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- Institute of Arthritis Research in Integrative Medicine, Shanghai Academy of Traditional Chinese Medicine, Shanghai, China
| | - Yu Shan
- Department of Rheumatology, Shanghai Guanghua Hospital of Integrative Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- Guanghua Clinical Medical College, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- Institute of Arthritis Research in Integrative Medicine, Shanghai Academy of Traditional Chinese Medicine, Shanghai, China
| | - Yixin Zheng
- Department of Rheumatology, Shanghai Guanghua Hospital of Integrative Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- Guanghua Clinical Medical College, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- Institute of Arthritis Research in Integrative Medicine, Shanghai Academy of Traditional Chinese Medicine, Shanghai, China
| | - Fuyu Zhao
- Department of Rheumatology, Shanghai Guanghua Hospital of Integrative Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- Guanghua Clinical Medical College, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- Institute of Arthritis Research in Integrative Medicine, Shanghai Academy of Traditional Chinese Medicine, Shanghai, China
| | - Xinliang Lv
- Traditional Chinese Medicine Hospital of Inner Mongolia Autonomous Region, Hohhot, Inner Mongolia Autonomous Region, China
| | - Shicheng Guo
- Department of Rheumatology, Shanghai Guanghua Hospital of Integrative Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Fubo Wang
- Guangxi Key Laboratory for Genomic and Personalized Medicine, Guangxi Collaborative Innovation Center for Genomic and Personalized Medicine, Guangxi Medical University, Nanning, Guangxi, China
- Department of Urology, Affiliated Tumor Hospital of Guangxi Medical University, Guangxi Medical University, Nanning, Guangxi, China
| | - Dongyi He
- Department of Rheumatology, Shanghai Guanghua Hospital of Integrative Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- Guanghua Clinical Medical College, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- Institute of Arthritis Research in Integrative Medicine, Shanghai Academy of Traditional Chinese Medicine, Shanghai, China
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15
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Cong Y, Endo T. A Quadruple Revolution: Deciphering Biological Complexity with Artificial Intelligence, Multiomics, Precision Medicine, and Planetary Health. OMICS : A JOURNAL OF INTEGRATIVE BIOLOGY 2024; 28:257-260. [PMID: 38813661 DOI: 10.1089/omi.2024.0110] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/31/2024]
Abstract
A quiet quadruple revolution has been in the making in systems science with convergence of (1) artificial intelligence, machine learning, and other digital technologies; (2) multiomics big data integration; (3) growing interest in the "variability science" of precision/personalized medicine that aims to account for patient-to-patient and between-population differences in disease susceptibilities and responses to health interventions such as drugs, nutrition, vaccines, and radiation; and (4) planetary health scholarship that both scales up and integrates biological, clinical, and ecological contexts of health and disease. Against this overarching background, this article presents and highlights some of the salient challenges and prospects of multiomics research, emphasizing the attendant pivotal role of systems medicine and systems biology. In addition, we emphasize the rapidly growing importance of planetary health research for systems medicine, particularly amid climate emergency, ecological degradation, and loss of planetary biodiversity. Looking ahead, we anticipate that the integration and utilization of multiomics big data and artificial intelligence will drive further progress in systems medicine and systems biology, heralding a promising future for both human and planetary health.
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Affiliation(s)
- Yi Cong
- Information Biology Laboratory, Graduate School of Information Science and Technology, Hokkaido University, Sapporo, Japan
| | - Toshinori Endo
- Information Biology Laboratory, Faculty of Information Science and Technology, Hokkaido University, Sapporo, Japan
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16
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Jardim LL, Schieber TA, Santana MP, Cerqueira MH, Lorenzato CS, Franco VKB, Zuccherato LW, da Silva Santos BA, Chaves DG, Ravetti MG, Rezende SM. Prediction of inhibitor development in previously untreated and minimally treated children with severe and moderately severe hemophilia A using a machine-learning network. J Thromb Haemost 2024:S1538-7836(24)00303-9. [PMID: 38810700 DOI: 10.1016/j.jtha.2024.05.017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2024] [Revised: 05/02/2024] [Accepted: 05/12/2024] [Indexed: 05/31/2024]
Abstract
BACKGROUND Prediction of inhibitor development in patients with hemophilia A (HA) remains a challenge. OBJECTIVES To construct a predictive model for inhibitor development in HA using a network of clinical variables and biomarkers based on the individual similarity network. METHODS Previously untreated and minimally treated children with severe/moderately severe HA, participants of the HEMFIL Cohort Study, were followed up until reaching 75 exposure days (EDs) without inhibitor (INH-) or upon inhibitor development (INH+). Clinical data and biological samples were collected before the start of factor (F)VIII replacement (T0). A predictive model (HemfilNET) was built to compare the networks and potential global topological differences between INH- and INH+ at T0, considering the network robustness. For validation, the "leave-one-out" cross-validation technique was employed. Accuracy, precision, recall, and F1-score were used as evaluation metrics for the machine-learning model. RESULTS We included 95 children with HA (CHA), of whom 31 (33%) developed inhibitors. The algorithm, featuring 37 variables, identified distinct patterns of networks at T0 for INH+ and INH-. The accuracy of the model was 74.2% for CHA INH+ and 98.4% for INH-. By focusing the analysis on CHA with high-risk F8 mutations for inhibitor development, the accuracy in identifying CHA INH+ increased to 82.1%. CONCLUSION Our machine-learning algorithm demonstrated an overall accuracy of 90.5% for predicting inhibitor development in CHA, which further improved when restricting the analysis to CHA with a high-risk F8 genotype. However, our model requires validation in other cohorts. Yet, missing data for some variables hindered more precise predictions.
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Affiliation(s)
- Letícia Lemos Jardim
- Instituto René Rachou (Fiocruz Minas), Belo Horizonte, Minas Gerais, Brazil; Department of Clinical Epidemiology, Leiden University Medical Centre, Leiden, the Netherlands
| | - Tiago A Schieber
- Faculdade de Ciências Econômicas, School of Economics, Universidade Federal de Minas Gerais, Brazil
| | | | | | | | | | | | | | | | - Martín Gomez Ravetti
- Departamento de Ciência da Computação, Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais, Brazil
| | - Suely Meireles Rezende
- Faculty of Medicine, Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais, Brazil.
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Alsulimani A, Akhter N, Jameela F, Ashgar RI, Jawed A, Hassani MA, Dar SA. The Impact of Artificial Intelligence on Microbial Diagnosis. Microorganisms 2024; 12:1051. [PMID: 38930432 PMCID: PMC11205376 DOI: 10.3390/microorganisms12061051] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2024] [Revised: 05/19/2024] [Accepted: 05/21/2024] [Indexed: 06/28/2024] Open
Abstract
Traditional microbial diagnostic methods face many obstacles such as sample handling, culture difficulties, misidentification, and delays in determining susceptibility. The advent of artificial intelligence (AI) has markedly transformed microbial diagnostics with rapid and precise analyses. Nonetheless, ethical considerations accompany AI adoption, necessitating measures to uphold patient privacy, mitigate biases, and ensure data integrity. This review examines conventional diagnostic hurdles, stressing the significance of standardized procedures in sample processing. It underscores AI's significant impact, particularly through machine learning (ML), in microbial diagnostics. Recent progressions in AI, particularly ML methodologies, are explored, showcasing their influence on microbial categorization, comprehension of microorganism interactions, and augmentation of microscopy capabilities. This review furnishes a comprehensive evaluation of AI's utility in microbial diagnostics, addressing both advantages and challenges. A few case studies including SARS-CoV-2, malaria, and mycobacteria serve to illustrate AI's potential for swift and precise diagnosis. Utilization of convolutional neural networks (CNNs) in digital pathology, automated bacterial classification, and colony counting further underscores AI's versatility. Additionally, AI improves antimicrobial susceptibility assessment and contributes to disease surveillance, outbreak forecasting, and real-time monitoring. Despite a few limitations, integration of AI in diagnostic microbiology presents robust solutions, user-friendly algorithms, and comprehensive training, promising paradigm-shifting advancements in healthcare.
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Affiliation(s)
- Ahmad Alsulimani
- Medical Laboratory Technology Department, College of Applied Medical Sciences, Jazan University, Jazan 45142, Saudi Arabia; (A.A.); (M.A.H.)
| | - Naseem Akhter
- Department of Biology, Arizona State University, Lake Havasu City, AZ 86403, USA;
| | - Fatima Jameela
- Modern American Dental Clinic, West Warren Avenue, Dearborn, MI 48126, USA;
| | - Rnda I. Ashgar
- College of Nursing, Jazan University, Jazan 45142, Saudi Arabia; (R.I.A.); (A.J.)
| | - Arshad Jawed
- College of Nursing, Jazan University, Jazan 45142, Saudi Arabia; (R.I.A.); (A.J.)
| | - Mohammed Ahmed Hassani
- Medical Laboratory Technology Department, College of Applied Medical Sciences, Jazan University, Jazan 45142, Saudi Arabia; (A.A.); (M.A.H.)
| | - Sajad Ahmad Dar
- College of Nursing, Jazan University, Jazan 45142, Saudi Arabia; (R.I.A.); (A.J.)
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18
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Huang YJ, Chen CH, Yang HC. AI-enhanced integration of genetic and medical imaging data for risk assessment of Type 2 diabetes. Nat Commun 2024; 15:4230. [PMID: 38762475 PMCID: PMC11102564 DOI: 10.1038/s41467-024-48618-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2023] [Accepted: 05/08/2024] [Indexed: 05/20/2024] Open
Abstract
Type 2 diabetes (T2D) presents a formidable global health challenge, highlighted by its escalating prevalence, underscoring the critical need for precision health strategies and early detection initiatives. Leveraging artificial intelligence, particularly eXtreme Gradient Boosting (XGBoost), we devise robust risk assessment models for T2D. Drawing upon comprehensive genetic and medical imaging datasets from 68,911 individuals in the Taiwan Biobank, our models integrate Polygenic Risk Scores (PRS), Multi-image Risk Scores (MRS), and demographic variables, such as age, sex, and T2D family history. Here, we show that our model achieves an Area Under the Receiver Operating Curve (AUC) of 0.94, effectively identifying high-risk T2D subgroups. A streamlined model featuring eight key variables also maintains a high AUC of 0.939. This high accuracy for T2D risk assessment promises to catalyze early detection and preventive strategies. Moreover, we introduce an accessible online risk assessment tool for T2D, facilitating broader applicability and dissemination of our findings.
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Affiliation(s)
- Yi-Jia Huang
- Institute of Public Health, National Yang-Ming Chiao-Tung University, Taipei, Taiwan
- Institute of Statistical Science, Academia Sinica, Taipei, Taiwan
| | - Chun-Houh Chen
- Institute of Statistical Science, Academia Sinica, Taipei, Taiwan
| | - Hsin-Chou Yang
- Institute of Public Health, National Yang-Ming Chiao-Tung University, Taipei, Taiwan.
- Institute of Statistical Science, Academia Sinica, Taipei, Taiwan.
- Biomedical Translation Research Center, Academia Sinica, Taipei, Taiwan.
- Department of Statistics, National Cheng Kung University, Tainan, Taiwan.
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Taiwo OR, Onyeaka H, Oladipo EK, Oloke JK, Chukwugozie DC. Advancements in Predictive Microbiology: Integrating New Technologies for Efficient Food Safety Models. Int J Microbiol 2024; 2024:6612162. [PMID: 38799770 PMCID: PMC11126350 DOI: 10.1155/2024/6612162] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2023] [Revised: 04/01/2024] [Accepted: 04/23/2024] [Indexed: 05/29/2024] Open
Abstract
Predictive microbiology is a rapidly evolving field that has gained significant interest over the years due to its diverse application in food safety. Predictive models are widely used in food microbiology to estimate the growth of microorganisms in food products. These models represent the dynamic interactions between intrinsic and extrinsic food factors as mathematical equations and then apply these data to predict shelf life, spoilage, and microbial risk assessment. Due to their ability to predict the microbial risk, these tools are also integrated into hazard analysis critical control point (HACCP) protocols. However, like most new technologies, several limitations have been linked to their use. Predictive models have been found incapable of modeling the intricate microbial interactions in food colonized by different bacteria populations under dynamic environmental conditions. To address this issue, researchers are integrating several new technologies into predictive models to improve efficiency and accuracy. Increasingly, newer technologies such as whole genome sequencing (WGS), metagenomics, artificial intelligence, and machine learning are being rapidly adopted into newer-generation models. This has facilitated the development of devices based on robotics, the Internet of Things, and time-temperature indicators that are being incorporated into food processing both domestically and industrially globally. This study reviewed current research on predictive models, limitations, challenges, and newer technologies being integrated into developing more efficient models. Machine learning algorithms commonly employed in predictive modeling are discussed with emphasis on their application in research and industry and their advantages over traditional models.
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Affiliation(s)
| | - Helen Onyeaka
- School of Chemical Engineering, University of Birmingham, Edgbaston B15 2TT, Birmingham, UK
| | - Elijah K. Oladipo
- Genomics Unit, Helix Biogen Institute, Ogbomosho, Oyo, Nigeria
- Department of Microbiology, Laboratory of Molecular Biology, Immunology and Bioinformatics, Adeleke University, Ede, Osun, Nigeria
| | - Julius Kola Oloke
- Department of Natural Science, Microbiology Unit, Precious Cornerstone University, Ibadan, Oyo, Nigeria
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20
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Martina M, Zayas A, Portis E, Di Nardo G, Polli MF, Comino C, Gilardi G, Martin E, Acquadro A. The Dark Side of the pollen: BSA-seq identified genomic regions linked to male sterility in globe artichoke. BMC PLANT BIOLOGY 2024; 24:415. [PMID: 38760683 PMCID: PMC11100218 DOI: 10.1186/s12870-024-05119-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/13/2023] [Accepted: 05/08/2024] [Indexed: 05/19/2024]
Abstract
Globe artichoke (Cynara cardunculus var. scolymus; 2n = 2x = 34) is a food crop consumed for its immature flower heads. Traditionally, globe artichoke varietal types are vegetatively propagated. However, seed propagation makes it possible to treat the crop as annual, increasing field uniformity and reducing farmers costs, as well as pathogens diffusion. Despite globe artichoke's significant agricultural value and the critical role of heterosis in the development of superior varieties, the production of hybrids remains challenging without a reliable system for large-scale industrial seed production. Male sterility (MS) presents a promising avenue for overcoming these challenges by simplifying the hybridization process and enabling cost-effective seed production. However, within the Cynara genus, genic male sterility has been linked to three recessive loci in globe artichoke, with no definitive genetic mechanism elucidated to date. A 250 offsprings F2 population, derived from a cross between a MS globe artichoke and a male fertile (MF) cultivated cardoon (C. cardunculus var. altilis) and fitting a monogenic segregation model (3:1), was analyzed through BSA-seq, aiming at the identification of genomic regions/genes affecting male sterility. Four QTL regions were identified on chromosomes 4, 12, and 14. By analyzing the sequence around the highest pick on chromosome 14, a cytochrome P450 (CYP703A2) was identified, carrying a deleterious substitution (R/Q) fixed in the male sterile parent. A single dCAPS marker was developed around this SNP, allowing the discrimination between MS and MF genotypes within the population, suitable for applications in plant breeding programs. A 3D model of the protein was generated by homology modeling, revealing that the mutated amino acid is part of a highly conserved motif crucial for protein folding.
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Affiliation(s)
- Matteo Martina
- DISAFA, Plant Genetics and Breeding, University of Turin, Turin, Italy
| | - Aldana Zayas
- IICAR (Instituto de Investigaciones en Ciencias Agrarias de Rosario), CONICET, Campo Exp. J.F. Villarino, Zavalla, Santa Fe, Argentina
| | - Ezio Portis
- DISAFA, Plant Genetics and Breeding, University of Turin, Turin, Italy
| | - Giovanna Di Nardo
- DBIOS, Department of Life Sciences and Systems Biology, University of Turin, Turin, Italy
| | | | - Cinzia Comino
- DISAFA, Plant Genetics and Breeding, University of Turin, Turin, Italy
| | - Gianfranco Gilardi
- DBIOS, Department of Life Sciences and Systems Biology, University of Turin, Turin, Italy
| | - Eugenia Martin
- IICAR (Instituto de Investigaciones en Ciencias Agrarias de Rosario), CONICET, Campo Exp. J.F. Villarino, Zavalla, Santa Fe, Argentina.
| | - Alberto Acquadro
- DISAFA, Plant Genetics and Breeding, University of Turin, Turin, Italy.
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21
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Cortés AJ. Abiotic Stress Tolerance Boosted by Genetic Diversity in Plants. Int J Mol Sci 2024; 25:5367. [PMID: 38791404 PMCID: PMC11121514 DOI: 10.3390/ijms25105367] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/29/2024] [Accepted: 03/14/2024] [Indexed: 05/26/2024] Open
Abstract
Plant breeding [...].
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Affiliation(s)
- Andrés J. Cortés
- Corporación Colombiana de Investigación Agropecuaria AGROSAVIA, C.I. La Selva, Km 7 vía Rionegro—Las Palmas, Rionegro 054048, Colombia;
- Facultad de Ciencias Agrarias—de Ciencias Forestales, Universidad Nacional de Colombia—Sede Medellín, Medellín 050034, Colombia
- Department of Plant Breeding, Swedish University of Agricultural Sciences, Lomma 23436, Sweden
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22
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Golbabaei MH, Varnoosfaderani MS, Hemmati F, Barati MR, Pishbin F, Seyyed Ebrahimi SA. Machine learning-guided morphological property prediction of 2D electrospun scaffolds: the effect of polymer chemical composition and processing parameters. RSC Adv 2024; 14:15178-15199. [PMID: 38737974 PMCID: PMC11082644 DOI: 10.1039/d4ra01257g] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2024] [Accepted: 04/27/2024] [Indexed: 05/14/2024] Open
Abstract
Among various methods for fabricating polymeric tissue engineering scaffolds, electrospinning stands out as a relatively simple technique widely utilized in research. Numerous studies have delved into understanding how electrospinning processing parameters and specific polymeric solutions affect the physical features of the resulting scaffolds. However, owing to the complexity of these interactions, no definitive approaches have emerged. This study introduces the use of Simplified Molecular Input Line Entry System (SMILES) encoding method to represent materials, coupled with machine learning algorithms, to model the relationships between material properties, electrospinning parameters and scaffolds' physical properties. Here, the scaffolds' fiber diameter and conductivity have been predicted for the first time using this approach. In the classification task, the voting classifier predicted the fibers diameter with a balanced accuracy score of 0.9478. In the regression task, a neural network regressor was architected to learn the relations between parameters and predict the fibers diameter with R2 = 0.723. In the case of fibers conductivity, regressor and classifier models were used for prediction, but the performance fluctuated due to the inadequate information in the published data and the collected dataset. Finally, the model prediction accuracy was validated by experimental electrospinning of a biocompatible polymer (i.e., polyvinyl alcohol and polyvinyl alcohol/polypyrrole). Field-emission scanning electron microscope (FE-SEM) images were used to measure fiber diameter. These results demonstrated the efficacy of the proposed model in predicting the polymer nanofiber diameter and reducing the parameter space prior to the scoping exercises. This data-driven model can be readily extended to the electrospinning of various biopolymers.
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Affiliation(s)
- Mohammad Hossein Golbabaei
- School of Metallurgy and Materials Engineering, College of Engineering, University of Tehran Tehran Iran +98 21 88006076 +98 21 61114065
| | | | - Farshid Hemmati
- School of Metallurgy and Materials Engineering, College of Engineering, University of Tehran Tehran Iran +98 21 88006076 +98 21 61114065
| | - Mohammad Reza Barati
- School of Metallurgy and Materials Engineering, College of Engineering, University of Tehran Tehran Iran +98 21 88006076 +98 21 61114065
| | - Fatemehsadat Pishbin
- School of Metallurgy and Materials Engineering, College of Engineering, University of Tehran Tehran Iran +98 21 88006076 +98 21 61114065
| | - Seyyed Ali Seyyed Ebrahimi
- Advanced Magnetic Materials Research Center, School of Metallurgy and Materials Engineering, College of Engineering, University of Tehran Tehran Iran +98 21 88006076 +98 21 88225374
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23
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Al-Zubayer MA, Alam K, Shanto HH, Maniruzzaman M, Majumder UK, Ahammed B. Machine learning models for prediction of double and triple burdens of non-communicable diseases in Bangladesh. J Biosoc Sci 2024; 56:426-444. [PMID: 38505939 DOI: 10.1017/s0021932024000063] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/21/2024]
Abstract
Increasing prevalence of non-communicable diseases (NCDs) has become the leading cause of death and disability in Bangladesh. Therefore, this study aimed to measure the prevalence of and risk factors for double and triple burden of NCDs (DBNCDs and TBNCDs), considering diabetes, hypertension, and overweight and obesity as well as establish a machine learning approach for predicting DBNCDs and TBNCDs. A total of 12,151 respondents from the 2017 to 2018 Bangladesh Demographic and Health Survey were included in this analysis, where 10%, 27.4%, and 24.3% of respondents had diabetes, hypertension, and overweight and obesity, respectively. Chi-square test and multilevel logistic regression (LR) analysis were applied to select factors associated with DBNCDs and TBNCDs. Furthermore, six classifiers including decision tree (DT), LR, naïve Bayes (NB), k-nearest neighbour (KNN), random forest (RF), and extreme gradient boosting (XGBoost) with three cross-validation protocols (K2, K5, and K10) were adopted to predict the status of DBNCDs and TBNCDs. The classification accuracy (ACC) and area under the curve (AUC) were computed for each protocol and repeated 10 times to make them more robust, and then the average ACC and AUC were computed. The prevalence of DBNCDs and TBNCDs was 14.3% and 2.3%, respectively. The findings of this study revealed that DBNCDs and TBNCDs were significantly influenced by age, sex, marital status, wealth index, education and geographic region. Compared to other classifiers, the RF-based classifier provides the highest ACC and AUC for both DBNCDs (ACC = 81.06% and AUC = 0.93) and TBNCDs (ACC = 88.61% and AUC = 0.97) for the K10 protocol. A combination of considered two-step factor selections and RF-based classifier can better predict the burden of NCDs. The findings of this study suggested that decision-makers might adopt suitable decisions to control and prevent the burden of NCDs using RF classifiers.
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Affiliation(s)
| | - Khorshed Alam
- School of Business, University of Southern Queensland, Toowoomba, QLD, Australia
- Centre for Health Research, University of Southern Queensland, Toowoomba, QLD, Australia
| | | | - Md Maniruzzaman
- Statistics Discipline, Khulna University, Khulna, Bangladesh
| | | | - Benojir Ahammed
- Statistics Discipline, Khulna University, Khulna, Bangladesh
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24
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Lodema DY, Ditzel FL, Hut SCA, van Dellen E, Otte WM, Slooter AJC. Single-channel qEEG characteristics distinguish delirium from no delirium, but not postoperative from non-postoperative delirium. Clin Neurophysiol 2024; 161:93-100. [PMID: 38460221 DOI: 10.1016/j.clinph.2024.01.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2023] [Revised: 12/06/2023] [Accepted: 01/19/2024] [Indexed: 03/11/2024]
Abstract
OBJECTIVE This exploratory study examined quantitative electroencephalography (qEEG) changes in delirium and the use of qEEG features to distinguish postoperative from non-postoperative delirium. METHODS This project was part of the DeltaStudy, a cross-sectional,multicenterstudy in Intensive Care Units (ICUs) and non-ICU wards. Single-channel (Fp2-Pz) four-minutes resting-state EEG was analyzed in 456 patients. After calculating 98 qEEG features per epoch, random forest (RF) classification was used to analyze qEEG changes in delirium and to test whether postoperative and non-postoperative delirium could be distinguished. RESULTS An area under the receiver operatingcharacteristic curve (AUC) of 0.76 (95% Confidence Interval (CI) 0.71-0.80) was found when classifying delirium with a sensitivity of 0.77 and a specificity of 0.63 at the optimal operating point. The classification of postoperative versus non-postoperative delirium resulted in an AUC of 0.50 (95%CI 0.38-0.61). CONCLUSIONS RF classification was able to discriminate delirium from no delirium with reasonable accuracy, while also identifying new delirium qEEG markers like autocorrelation and theta peak frequency. RF classification could not distinguish postoperative from non-postoperative delirium. SIGNIFICANCE Single-channel EEG differentiates between delirium and no delirium with reasonable accuracy. We found no distinct EEG profile for postoperative delirium, which may suggest that delirium is one entity, whether it develops postoperatively or not.
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Affiliation(s)
- D Y Lodema
- Department of Intensive Care Medicine and University Medical Center Utrecht Brain Center, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands; Department of Psychiatry and University Medical Center Utrecht Brain Center, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands.
| | - F L Ditzel
- Department of Intensive Care Medicine and University Medical Center Utrecht Brain Center, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands
| | - S C A Hut
- Department of Intensive Care Medicine and University Medical Center Utrecht Brain Center, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands
| | - E van Dellen
- Department of Psychiatry and University Medical Center Utrecht Brain Center, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands; Department of Neurology, UZ Brussel and Vrije Universiteit Brussel, Brussels, Belgium
| | - W M Otte
- Department of Pediatric Neurology and University Medical Center Utrecht Brain Center, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands
| | - A J C Slooter
- Department of Intensive Care Medicine and University Medical Center Utrecht Brain Center, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands; Department of Psychiatry and University Medical Center Utrecht Brain Center, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands; Department of Neurology, UZ Brussel and Vrije Universiteit Brussel, Brussels, Belgium
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25
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Fussner S, Boyne A, Han A, Nakhleh LA, Haneef Z. Differentiating Epileptic and Psychogenic Non-Epileptic Seizures Using Machine Learning Analysis of EEG Plot Images. SENSORS (BASEL, SWITZERLAND) 2024; 24:2823. [PMID: 38732929 PMCID: PMC11086151 DOI: 10.3390/s24092823] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/29/2024] [Revised: 04/22/2024] [Accepted: 04/28/2024] [Indexed: 05/13/2024]
Abstract
The treatment of epilepsy, the second most common chronic neurological disorder, is often complicated by the failure of patients to respond to medication. Treatment failure with anti-seizure medications is often due to the presence of non-epileptic seizures. Distinguishing non-epileptic from epileptic seizures requires an expensive and time-consuming analysis of electroencephalograms (EEGs) recorded in an epilepsy monitoring unit. Machine learning algorithms have been used to detect seizures from EEG, typically using EEG waveform analysis. We employed an alternative approach, using a convolutional neural network (CNN) with transfer learning using MobileNetV2 to emulate the real-world visual analysis of EEG images by epileptologists. A total of 5359 EEG waveform plot images from 107 adult subjects across two epilepsy monitoring units in separate medical facilities were divided into epileptic and non-epileptic groups for training and cross-validation of the CNN. The model achieved an accuracy of 86.9% (Area Under the Curve, AUC 0.92) at the site where training data were extracted and an accuracy of 87.3% (AUC 0.94) at the other site whose data were only used for validation. This investigation demonstrates the high accuracy achievable with CNN analysis of EEG plot images and the robustness of this approach across EEG visualization software, laying the groundwork for further subclassification of seizures using similar approaches in a clinical setting.
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Affiliation(s)
- Steven Fussner
- Department of Neurology, Baylor College of Medicine, Houston, TX 77030, USA
| | - Aidan Boyne
- Undergraduate Medical Education, Baylor College of Medicine, Houston, TX 77030, USA
| | - Albert Han
- Undergraduate Medical Education, Baylor College of Medicine, Houston, TX 77030, USA
| | - Lauren A. Nakhleh
- Undergraduate Medical Education, Baylor College of Medicine, Houston, TX 77030, USA
| | - Zulfi Haneef
- Department of Neurology, Baylor College of Medicine, Houston, TX 77030, USA
- Neurology Care Line, Michael E. DeBakey VA Medical Center, Houston, TX 77030, USA
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26
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Abbasi AF, Asim MN, Ahmed S, Dengel A. Long extrachromosomal circular DNA identification by fusing sequence-derived features of physicochemical properties and nucleotide distribution patterns. Sci Rep 2024; 14:9466. [PMID: 38658614 PMCID: PMC11043385 DOI: 10.1038/s41598-024-57457-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2023] [Accepted: 03/18/2024] [Indexed: 04/26/2024] Open
Abstract
Long extrachromosomal circular DNA (leccDNA) regulates several biological processes such as genomic instability, gene amplification, and oncogenesis. The identification of leccDNA holds significant importance to investigate its potential associations with cancer, autoimmune, cardiovascular, and neurological diseases. In addition, understanding these associations can provide valuable insights about disease mechanisms and potential therapeutic approaches. Conventionally, wet lab-based methods are utilized to identify leccDNA, which are hindered by the need for prior knowledge, and resource-intensive processes, potentially limiting their broader applicability. To empower the process of leccDNA identification across multiple species, the paper in hand presents the very first computational predictor. The proposed iLEC-DNA predictor makes use of SVM classifier along with sequence-derived nucleotide distribution patterns and physicochemical properties-based features. In addition, the study introduces a set of 12 benchmark leccDNA datasets related to three species, namely Homo sapiens (HM), Arabidopsis Thaliana (AT), and Saccharomyces cerevisiae (SC/YS). It performs large-scale experimentation across 12 benchmark datasets under different experimental settings using the proposed predictor, more than 140 baseline predictors, and 858 encoder ensembles. The proposed predictor outperforms baseline predictors and encoder ensembles across diverse leccDNA datasets by producing average performance values of 81.09%, 62.2% and 81.08% in terms of ACC, MCC and AUC-ROC across all the datasets. The source code of the proposed and baseline predictors is available at https://github.com/FAhtisham/Extrachrosmosomal-DNA-Prediction . To facilitate the scientific community, a web application for leccDNA identification is available at https://sds_genetic_analysis.opendfki.de/iLEC_DNA/.
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Affiliation(s)
- Ahtisham Fazeel Abbasi
- Department of Computer Science, Rhineland-Palatinate Technical University of Kaiserslautern-Landau, 67663, Kaiserslautern, Germany.
- German Research Center for Artificial Intelligence GmbH, 67663, Kaiserslautern, Germany.
| | - Muhammad Nabeel Asim
- German Research Center for Artificial Intelligence GmbH, 67663, Kaiserslautern, Germany.
| | - Sheraz Ahmed
- German Research Center for Artificial Intelligence GmbH, 67663, Kaiserslautern, Germany
| | - Andreas Dengel
- Department of Computer Science, Rhineland-Palatinate Technical University of Kaiserslautern-Landau, 67663, Kaiserslautern, Germany
- German Research Center for Artificial Intelligence GmbH, 67663, Kaiserslautern, Germany
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27
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Ranganathan S, Dee EC, Debnath N, Patel TA, Jain B, Murthy V. Access and barriers to genomic classifiers for breast cancer and prostate cancer in India. Int J Cancer 2024; 154:1335-1339. [PMID: 37962056 DOI: 10.1002/ijc.34784] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2023] [Revised: 09/25/2023] [Accepted: 10/10/2023] [Indexed: 11/15/2023]
Abstract
The incidence of cancer in general, including breast and prostate cancer specifically, is increasing in India. Breast and prostate cancers have genomic classifiers developed to guide therapy decisions. However, these genomic classifiers are often inaccessible in India due to high cost. These classifiers may also be less suitable to the Indian population, as data primarily from patients in wealthy Western countries were used in developing these genomic classifiers. In addition to the limitations in using these existing genomic classifiers, developing and validating new genomic classifiers for breast and prostate cancer in India is challenging due to the heterogeneity in the Indian population. However, there are steps that can be taken to address the various barriers that currently exist for accurate, accessible genomic classifiers for cancer in India.
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Affiliation(s)
| | - Edward Christopher Dee
- Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Neha Debnath
- Department of Medicine, Icahn School of Medicine at Mount Sinai (Morningside/West), New York, New York, USA
| | - Tej A Patel
- Department of Healthcare Management & Policy, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Bhav Jain
- Department of Health Policy, Stanford University School of Medicine, Stanford, California, USA
| | - Vedang Murthy
- Department of Radiation Oncology, ACTREC, Tata Memorial Centre, Homi Bhabha National Institute, Mumbai, India
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28
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Baskaya F, Lemainque T, Klinkhammer B, Koletnik S, von Stillfried S, Talbot SR, Boor P, Schulz V, Lederle W, Kiessling F. Pathophysiologic Mapping of Chronic Liver Diseases With Longitudinal Multiparametric MRI in Animal Models. Invest Radiol 2024:00004424-990000000-00209. [PMID: 38598653 DOI: 10.1097/rli.0000000000001075] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/12/2024]
Abstract
OBJECTIVES Chronic liver diseases (CLDs) have diverse etiologies. To better classify CLDs, we explored the ability of longitudinal multiparametric MRI (magnetic resonance imaging) in depicting alterations in liver morphology, inflammation, and hepatocyte and macrophage activity in murine high-fat diet (HFD)- and carbon tetrachloride (CCl4)-induced CLD models. MATERIALS AND METHODS Mice were either untreated, fed an HFD for 24 weeks, or injected with CCl4 for 8 weeks. Longitudinal multiparametric MRI was performed every 4 weeks using a 7 T MRI scanner, including T1/T2 relaxometry, morphological T1/T2-weighted imaging, and fat-selective imaging. Diffusion-weighted imaging was applied to assess fibrotic remodeling and T1-weighted and T2*-weighted dynamic contrast-enhanced MRI and dynamic susceptibility contrast MRI using gadoxetic acid and ferucarbotran to target hepatocytes and the mononuclear phagocyte system, respectively. Imaging data were associated with histopathological and serological analyses. Principal component analysis and clustering were used to reveal underlying disease patterns. RESULTS The MRI parameters significantly correlated with histologically confirmed steatosis, fibrosis, and liver damage, with varying importance. No single MRI parameter exclusively correlated with 1 pathophysiological feature, underscoring the necessity for using parameter patterns. Clustering revealed early-stage, model-specific patterns. Although the HFD model exhibited pronounced liver fat content and fibrosis, the CCl4 model indicated reduced liver fat content and impaired hepatocyte and macrophage function. In both models, MRI biomarkers of inflammation were elevated. CONCLUSIONS Multiparametric MRI patterns can be assigned to pathophysiological processes and used for murine CLD classification and progression tracking. These MRI biomarker patterns can directly be explored clinically to improve early CLD detection and differentiation and to refine treatments.
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Affiliation(s)
- Ferhan Baskaya
- From the Institute for Experimental Molecular Imaging, RWTH Aachen University, Aachen, Germany (F.B., T.L., S.K., V.S., W.L., F.K.); Department for Diagnostic and Interventional Radiology, RWTH Aachen University, Aachen, Germany (T.L.); Institute of Pathology, RWTH Aachen University, Aachen, Germany (B.K., S.S., P.B.); and Institute for Laboratory Animal Science, Hannover Medical School, Hannover, Germany (S.R.T.)
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29
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Sgueglia G, Vrettas MD, Chino M, De Simone A, Lombardi A. MetalHawk: Enhanced Classification of Metal Coordination Geometries by Artificial Neural Networks. J Chem Inf Model 2024; 64:2356-2367. [PMID: 37956388 PMCID: PMC11005052 DOI: 10.1021/acs.jcim.3c00873] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2023] [Revised: 09/29/2023] [Accepted: 10/26/2023] [Indexed: 11/15/2023]
Abstract
The chemical properties of metal complexes are strongly dependent on the number and geometrical arrangement of ligands coordinated to the metal center. Existing methods for determining either coordination number or geometry rely on a trade-off between accuracy and computational costs, which hinders their application to the study of large structure data sets. Here, we propose MetalHawk (https://github.com/vrettasm/MetalHawk), a machine learning-based approach to perform simultaneous classification of metal site coordination number and geometry through artificial neural networks (ANNs), which were trained using the Cambridge Structural Database (CSD) and Metal Protein Data Bank (MetalPDB). We demonstrate that the CSD-trained model can be used to classify sites belonging to the most common coordination numbers and geometry classes with balanced accuracy equal to 96.51% for CSD-deposited metal sites. The CSD-trained model was also found to be capable of classifying bioinorganic metal sites from the MetalPDB database, with balanced accuracy equal to 84.29% on the whole PDB data set and to 91.66% on manually reviewed sites in the PDB validation set. Moreover, we report evidence that the output vectors of the CSD-trained model can be considered as a proxy indicator of metal-site distortions, showing that these can be interpreted as a low-dimensional representation of subtle geometrical features present in metal site structures.
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Affiliation(s)
- Gianmattia Sgueglia
- Department
of Chemical Sciences, University of Naples
Federico II, Via Cintia 21, 80126 Napoli, Italy
| | - Michail D. Vrettas
- Department
of Pharmacy, University of Naples Federico
II, Via Domenico Montesano
49, 80131 Napoli, Italy
| | - Marco Chino
- Department
of Chemical Sciences, University of Naples
Federico II, Via Cintia 21, 80126 Napoli, Italy
| | - Alfonso De Simone
- Department
of Pharmacy, University of Naples Federico
II, Via Domenico Montesano
49, 80131 Napoli, Italy
| | - Angela Lombardi
- Department
of Chemical Sciences, University of Naples
Federico II, Via Cintia 21, 80126 Napoli, Italy
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30
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Wang JH, Gessler DJ, Zhan W, Gallagher TL, Gao G. Adeno-associated virus as a delivery vector for gene therapy of human diseases. Signal Transduct Target Ther 2024; 9:78. [PMID: 38565561 PMCID: PMC10987683 DOI: 10.1038/s41392-024-01780-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2023] [Revised: 02/08/2024] [Accepted: 02/19/2024] [Indexed: 04/04/2024] Open
Abstract
Adeno-associated virus (AAV) has emerged as a pivotal delivery tool in clinical gene therapy owing to its minimal pathogenicity and ability to establish long-term gene expression in different tissues. Recombinant AAV (rAAV) has been engineered for enhanced specificity and developed as a tool for treating various diseases. However, as rAAV is being more widely used as a therapy, the increased demand has created challenges for the existing manufacturing methods. Seven rAAV-based gene therapy products have received regulatory approval, but there continue to be concerns about safely using high-dose viral therapies in humans, including immune responses and adverse effects such as genotoxicity, hepatotoxicity, thrombotic microangiopathy, and neurotoxicity. In this review, we explore AAV biology with an emphasis on current vector engineering strategies and manufacturing technologies. We discuss how rAAVs are being employed in ongoing clinical trials for ocular, neurological, metabolic, hematological, neuromuscular, and cardiovascular diseases as well as cancers. We outline immune responses triggered by rAAV, address associated side effects, and discuss strategies to mitigate these reactions. We hope that discussing recent advancements and current challenges in the field will be a helpful guide for researchers and clinicians navigating the ever-evolving landscape of rAAV-based gene therapy.
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Affiliation(s)
- Jiang-Hui Wang
- Horae Gene Therapy Center, University of Massachusetts Chan Medical School, Worcester, MA, 01605, USA
- Department of Microbiology and Physiological Systems, University of Massachusetts Chan Medical School, Worcester, MA, 01605, USA
- Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, East Melbourne, VIC, 3002, Australia
- Ophthalmology, Department of Surgery, University of Melbourne, East Melbourne, VIC, 3002, Australia
| | - Dominic J Gessler
- Horae Gene Therapy Center, University of Massachusetts Chan Medical School, Worcester, MA, 01605, USA
- Department of Microbiology and Physiological Systems, University of Massachusetts Chan Medical School, Worcester, MA, 01605, USA
- Department of Neurological Surgery, University of Massachusetts Chan Medical School, Worcester, MA, 01605, USA
- Department of Neurosurgery, University of Minnesota, Minneapolis, MN, 55455, USA
| | - Wei Zhan
- Horae Gene Therapy Center, University of Massachusetts Chan Medical School, Worcester, MA, 01605, USA
- Department of Microbiology and Physiological Systems, University of Massachusetts Chan Medical School, Worcester, MA, 01605, USA
- Department of Medicine, University of Massachusetts Chan Medical School, Worcester, MA, 01605, USA
- Li Weibo Institute for Rare Diseases Research, University of Massachusetts Chan Medical School, Worcester, MA, 01605, USA
| | - Thomas L Gallagher
- Horae Gene Therapy Center, University of Massachusetts Chan Medical School, Worcester, MA, 01605, USA
| | - Guangping Gao
- Horae Gene Therapy Center, University of Massachusetts Chan Medical School, Worcester, MA, 01605, USA.
- Department of Microbiology and Physiological Systems, University of Massachusetts Chan Medical School, Worcester, MA, 01605, USA.
- Li Weibo Institute for Rare Diseases Research, University of Massachusetts Chan Medical School, Worcester, MA, 01605, USA.
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Coroller T, Sahiner B, Amatya A, Gossmann A, Karagiannis K, Moloney C, Samala RK, Santana-Quintero L, Solovieff N, Wang C, Amiri-Kordestani L, Cao Q, Cha KH, Charlab R, Cross FH, Hu T, Huang R, Kraft J, Krusche P, Li Y, Li Z, Mazo I, Paul R, Schnakenberg S, Serra P, Smith S, Song C, Su F, Tiwari M, Vechery C, Xiong X, Zarate JP, Zhu H, Chakravartty A, Liu Q, Ohlssen D, Petrick N, Schneider JA, Walderhaug M, Zuber E. Methodology for Good Machine Learning with Multi-Omics Data. Clin Pharmacol Ther 2024; 115:745-757. [PMID: 37965805 DOI: 10.1002/cpt.3105] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2023] [Accepted: 10/20/2023] [Indexed: 11/16/2023]
Abstract
In 2020, Novartis Pharmaceuticals Corporation and the U.S. Food and Drug Administration (FDA) started a 4-year scientific collaboration to approach complex new data modalities and advanced analytics. The scientific question was to find novel radio-genomics-based prognostic and predictive factors for HR+/HER- metastatic breast cancer under a Research Collaboration Agreement. This collaboration has been providing valuable insights to help successfully implement future scientific projects, particularly using artificial intelligence and machine learning. This tutorial aims to provide tangible guidelines for a multi-omics project that includes multidisciplinary expert teams, spanning across different institutions. We cover key ideas, such as "maintaining effective communication" and "following good data science practices," followed by the four steps of exploratory projects, namely (1) plan, (2) design, (3) develop, and (4) disseminate. We break each step into smaller concepts with strategies for implementation and provide illustrations from our collaboration to further give the readers actionable guidance.
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Affiliation(s)
| | - Berkman Sahiner
- Center for Devices and Radiological Health, U.S. Food and Drug Administration, Silver Spring, Maryland, USA
| | - Anup Amatya
- Center for Drug Evaluation and Research, U.S. Food and Drug Administration, Silver Spring, Maryland, USA
| | - Alexej Gossmann
- Center for Devices and Radiological Health, U.S. Food and Drug Administration, Silver Spring, Maryland, USA
| | - Konstantinos Karagiannis
- Center for Biologics Evaluation and Research, U.S. Food and Drug Administration, Silver Spring, Maryland, USA
| | | | - Ravi K Samala
- Center for Devices and Radiological Health, U.S. Food and Drug Administration, Silver Spring, Maryland, USA
| | - Luis Santana-Quintero
- Center for Biologics Evaluation and Research, U.S. Food and Drug Administration, Silver Spring, Maryland, USA
| | - Nadia Solovieff
- Novartis Pharmaceutical Company, East Hanover, New Jersey, USA
| | - Craig Wang
- Novartis Pharma AG, Rotkreuz, Switzerland
| | - Laleh Amiri-Kordestani
- Center for Drug Evaluation and Research, U.S. Food and Drug Administration, Silver Spring, Maryland, USA
| | - Qian Cao
- Center for Devices and Radiological Health, U.S. Food and Drug Administration, Silver Spring, Maryland, USA
| | - Kenny H Cha
- Center for Devices and Radiological Health, U.S. Food and Drug Administration, Silver Spring, Maryland, USA
| | - Rosane Charlab
- Center for Drug Evaluation and Research, U.S. Food and Drug Administration, Silver Spring, Maryland, USA
| | - Frank H Cross
- Center for Drug Evaluation and Research, U.S. Food and Drug Administration, Silver Spring, Maryland, USA
| | - Tingting Hu
- Center for Devices and Radiological Health, U.S. Food and Drug Administration, Silver Spring, Maryland, USA
| | - Ruihao Huang
- Center for Drug Evaluation and Research, U.S. Food and Drug Administration, Silver Spring, Maryland, USA
| | - Jeffrey Kraft
- Center for Drug Evaluation and Research, U.S. Food and Drug Administration, Silver Spring, Maryland, USA
| | | | - Yutong Li
- Novartis Pharmaceutical Company, East Hanover, New Jersey, USA
| | - Zheng Li
- Novartis Pharmaceutical Company, East Hanover, New Jersey, USA
| | - Ilya Mazo
- Center for Biologics Evaluation and Research, U.S. Food and Drug Administration, Silver Spring, Maryland, USA
| | - Rahul Paul
- Center for Biologics Evaluation and Research, U.S. Food and Drug Administration, Silver Spring, Maryland, USA
| | | | - Paolo Serra
- Novartis Pharmaceutical Company, East Hanover, New Jersey, USA
| | - Sean Smith
- Center for Biologics Evaluation and Research, U.S. Food and Drug Administration, Silver Spring, Maryland, USA
| | - Chi Song
- Center for Drug Evaluation and Research, U.S. Food and Drug Administration, Silver Spring, Maryland, USA
| | - Fei Su
- Novartis Pharmaceutical Company, East Hanover, New Jersey, USA
| | - Mohit Tiwari
- Center for Drug Evaluation and Research, U.S. Food and Drug Administration, Silver Spring, Maryland, USA
| | - Colin Vechery
- Novartis Pharmaceutical Company, East Hanover, New Jersey, USA
| | - Xin Xiong
- Center for Devices and Radiological Health, U.S. Food and Drug Administration, Silver Spring, Maryland, USA
| | | | - Hao Zhu
- Center for Drug Evaluation and Research, U.S. Food and Drug Administration, Silver Spring, Maryland, USA
| | | | - Qi Liu
- Center for Drug Evaluation and Research, U.S. Food and Drug Administration, Silver Spring, Maryland, USA
| | - David Ohlssen
- Novartis Pharmaceutical Company, East Hanover, New Jersey, USA
| | - Nicholas Petrick
- Center for Devices and Radiological Health, U.S. Food and Drug Administration, Silver Spring, Maryland, USA
| | - Julie A Schneider
- Oncology Center of Excellence, U.S. Food and Drug Administration, Silver Spring, Maryland, USA
| | - Mark Walderhaug
- Center for Biologics Evaluation and Research, U.S. Food and Drug Administration, Silver Spring, Maryland, USA
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Negm A, Ma X, Aggidis G. Deep reinforcement learning challenges and opportunities for urban water systems. WATER RESEARCH 2024; 253:121145. [PMID: 38330870 DOI: 10.1016/j.watres.2024.121145] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/20/2023] [Revised: 01/09/2024] [Accepted: 01/14/2024] [Indexed: 02/10/2024]
Abstract
The efficient and sustainable supply and transport of water is a key component to any functioning civilisation making the role of urban water systems (UWS) inherently crucial to the wellbeing of its customers. However, managing water is not a simple task. Whether it is ageing infrastructure, transient flows, air cavities or low pressures; water can be lost as a result of many issues that face UWSs. The complexity of those networks grows with the high urbanisation trends and climate change making water companies and regulatory bodies in need of new solutions. So, it comes as no surprise that many researchers are invested in innovating within the water industry to ensure that the future of our water is safe. Deep reinforcement learning (DRL) has the potential to tackle complexities that used to be very challenging as it relies on deep neural networks for function approximation and representation. This technology has conquered many fields due to its impressive results and can effectively revolutionise UWS. In this article, we explain the background of DRL and the milestones of this field using a novel taxonomy of the DRL algorithms. This will be followed by with a novel review of DRL applications in the UWS which focus on water distribution networks and stormwater systems. The review will be concluded with critical insights on how DRL can benefit different aspects of urban water systems.
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Affiliation(s)
- Ahmed Negm
- Lancaster University Energy Group, School of Engineering, Lancaster LA1 4YW, UK
| | - Xiandong Ma
- Lancaster University Energy Group, School of Engineering, Lancaster LA1 4YW, UK
| | - George Aggidis
- Lancaster University Energy Group, School of Engineering, Lancaster LA1 4YW, UK.
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Chen Z, Ain NU, Zhao Q, Zhang X. From tradition to innovation: conventional and deep learning frameworks in genome annotation. Brief Bioinform 2024; 25:bbae138. [PMID: 38581418 PMCID: PMC10998533 DOI: 10.1093/bib/bbae138] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2023] [Revised: 03/08/2024] [Accepted: 03/10/2024] [Indexed: 04/08/2024] Open
Abstract
Following the milestone success of the Human Genome Project, the 'Encyclopedia of DNA Elements (ENCODE)' initiative was launched in 2003 to unearth information about the numerous functional elements within the genome. This endeavor coincided with the emergence of numerous novel technologies, accompanied by the provision of vast amounts of whole-genome sequences, high-throughput data such as ChIP-Seq and RNA-Seq. Extracting biologically meaningful information from this massive dataset has become a critical aspect of many recent studies, particularly in annotating and predicting the functions of unknown genes. The core idea behind genome annotation is to identify genes and various functional elements within the genome sequence and infer their biological functions. Traditional wet-lab experimental methods still rely on extensive efforts for functional verification. However, early bioinformatics algorithms and software primarily employed shallow learning techniques; thus, the ability to characterize data and features learning was limited. With the widespread adoption of RNA-Seq technology, scientists from the biological community began to harness the potential of machine learning and deep learning approaches for gene structure prediction and functional annotation. In this context, we reviewed both conventional methods and contemporary deep learning frameworks, and highlighted novel perspectives on the challenges arising during annotation underscoring the dynamic nature of this evolving scientific landscape.
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Affiliation(s)
- Zhaojia Chen
- National Key Laboratory for Tropical Crop Breeding, Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, Guangzhou 518120, China
- College of Biomedical Engineering, Taiyuan University of Technology, Jinzhong 030600, China
| | - Noor ul Ain
- National Key Laboratory for Tropical Crop Breeding, Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, Guangzhou 518120, China
| | - Qian Zhao
- State Key Laboratory for Ecological Pest Control of Fujian/Taiwan Crops and College of Life Science, Fujian Agriculture and Forestry University, Fuzhou, 350002, China
| | - Xingtan Zhang
- National Key Laboratory for Tropical Crop Breeding, Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, Guangzhou 518120, China
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Liu X, Read SJ. Development of a multivariate prediction model for antidepressant resistant depression using reward-related predictors. Front Psychiatry 2024; 15:1349576. [PMID: 38590792 PMCID: PMC10999634 DOI: 10.3389/fpsyt.2024.1349576] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/04/2023] [Accepted: 03/11/2024] [Indexed: 04/10/2024] Open
Abstract
Introduction Individuals with depression who do not respond to two or more courses of serotonergic antidepressants tend to have greater deficits in reward processing and greater internalizing symptoms, yet there is no validated self-report method to determine the likelihood of treatment resistance based on these symptom dimensions. Methods This online case-control study leverages machine learning techniques to identify differences in self-reported anhedonia and internalizing symptom profiles of antidepressant non-responders compared to responders and healthy controls, as an initial proof-of-concept for relating these indicators to medication responsiveness. Random forest classifiers were used to identify a subset from a set of 24 reward predictors that distinguished among serotonergic medication resistant, non-resistant, and non-depressed individuals recruited online (N = 393). Feature selection was implemented to refine model prediction and improve interpretability. Results Accuracies for full predictor models ranged from .54 to .71, while feature selected models retained 3-5 predictors and generated accuracies of .42 to .70. Several models performed significantly above chance. Sensitivity for non-responders was greatest after feature selection when compared to only responders, reaching .82 with 3 predictors. The predictors retained from feature selection were then explored using factor analysis at the item level and cluster analysis of the full data to determine empirically driven data structures. Discussion Non-responders displayed 3 distinct symptom profiles along internalizing dimensions of anxiety, anhedonia, motivation, and cognitive function. Results should be replicated in a prospective cohort sample for predictive validity; however, this study demonstrates validity for using a limited anhedonia and internalizing self-report instrument for distinguishing between antidepressant resistant and responsive depression profiles.
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Affiliation(s)
- Xiao Liu
- Department of Psychology, University of Southern California, Los Angeles, CA, United States
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35
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Mota LFM, Arikawa LM, Santos SWB, Fernandes Júnior GA, Alves AAC, Rosa GJM, Mercadante MEZ, Cyrillo JNSG, Carvalheiro R, Albuquerque LG. Benchmarking machine learning and parametric methods for genomic prediction of feed efficiency-related traits in Nellore cattle. Sci Rep 2024; 14:6404. [PMID: 38493207 PMCID: PMC10944497 DOI: 10.1038/s41598-024-57234-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2023] [Accepted: 03/15/2024] [Indexed: 03/18/2024] Open
Abstract
Genomic selection (GS) offers a promising opportunity for selecting more efficient animals to use consumed energy for maintenance and growth functions, impacting profitability and environmental sustainability. Here, we compared the prediction accuracy of multi-layer neural network (MLNN) and support vector regression (SVR) against single-trait (STGBLUP), multi-trait genomic best linear unbiased prediction (MTGBLUP), and Bayesian regression (BayesA, BayesB, BayesC, BRR, and BLasso) for feed efficiency (FE) traits. FE-related traits were measured in 1156 Nellore cattle from an experimental breeding program genotyped for ~ 300 K markers after quality control. Prediction accuracy (Acc) was evaluated using a forward validation splitting the dataset based on birth year, considering the phenotypes adjusted for the fixed effects and covariates as pseudo-phenotypes. The MLNN and SVR approaches were trained by randomly splitting the training population into fivefold to select the best hyperparameters. The results show that the machine learning methods (MLNN and SVR) and MTGBLUP outperformed STGBLUP and the Bayesian regression approaches, increasing the Acc by approximately 8.9%, 14.6%, and 13.7% using MLNN, SVR, and MTGBLUP, respectively. Acc for SVR and MTGBLUP were slightly different, ranging from 0.62 to 0.69 and 0.62 to 0.68, respectively, with empirically unbiased for both models (0.97 and 1.09). Our results indicated that SVR and MTGBLUBP approaches were more accurate in predicting FE-related traits than Bayesian regression and STGBLUP and seemed competitive for GS of complex phenotypes with various degrees of inheritance.
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Affiliation(s)
- Lucio F M Mota
- School of Agricultural and Veterinarian Sciences, São Paulo State University (UNESP), Jaboticabal, SP, 14884-900, Brazil.
| | - Leonardo M Arikawa
- School of Agricultural and Veterinarian Sciences, São Paulo State University (UNESP), Jaboticabal, SP, 14884-900, Brazil
| | - Samuel W B Santos
- School of Agricultural and Veterinarian Sciences, São Paulo State University (UNESP), Jaboticabal, SP, 14884-900, Brazil
| | - Gerardo A Fernandes Júnior
- School of Agricultural and Veterinarian Sciences, São Paulo State University (UNESP), Jaboticabal, SP, 14884-900, Brazil
| | - Anderson A C Alves
- School of Agricultural and Veterinarian Sciences, São Paulo State University (UNESP), Jaboticabal, SP, 14884-900, Brazil
| | - Guilherme J M Rosa
- Department of Animal and Dairy Sciences, University of Wisconsin, Madison, WI, 53706, USA
| | - Maria E Z Mercadante
- Institute of Animal Science, Beef Cattle Research Center, Sertãozinho, SP, 14174-000, Brazil
- National Council for Science and Technological Development, Brasilia, DF, 71605-001, Brazil
| | - Joslaine N S G Cyrillo
- Institute of Animal Science, Beef Cattle Research Center, Sertãozinho, SP, 14174-000, Brazil
| | - Roberto Carvalheiro
- School of Agricultural and Veterinarian Sciences, São Paulo State University (UNESP), Jaboticabal, SP, 14884-900, Brazil
- National Council for Science and Technological Development, Brasilia, DF, 71605-001, Brazil
| | - Lucia G Albuquerque
- School of Agricultural and Veterinarian Sciences, São Paulo State University (UNESP), Jaboticabal, SP, 14884-900, Brazil.
- National Council for Science and Technological Development, Brasilia, DF, 71605-001, Brazil.
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Platt DE, Guzmán-Sáenz A, Bose A, Saha S, Utro F, Parida L. AI-enabled evaluation of genome-wide association relevance and polygenic risk score prediction in Alzheimer's disease. iScience 2024; 27:109209. [PMID: 38439972 PMCID: PMC10910245 DOI: 10.1016/j.isci.2024.109209] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Revised: 10/05/2023] [Accepted: 02/07/2024] [Indexed: 03/06/2024] Open
Abstract
GWAS focuses on significance loosing false positives; machine learning probes sub-significant features relying on predictivity. Yet, these are far from orthogonal. We sought to explore how these inform each other in sub-genome-wide significant situations to define relevance for predictive features. We introduce the SVM-based RubricOE that selects heavily cross-validated feature sets, and LDpred2 PRS as a strong contrast to SVM, to explore significance and predictivity. Our Alzheimer's test case notoriously lacks strong genetic signals except for few very strong phenotype-SNP associations, which suits the problem we are exploring. We found that the most significant SNPs among ML and PRS-selected SNPs captured most of the predictivity, while weaker associations tend also to contribute weakly to predictivity. SNPs with weak associations tend not to contribute to predictivity, but deletion of these features does not injure it. Significance provides a ranking that helps identify weakly predictive features.
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Affiliation(s)
- Daniel E. Platt
- IBM T. J. Watson Research Center, Yorktown Heights, New York, NY, USA
| | - Aldo Guzmán-Sáenz
- IBM T. J. Watson Research Center, Yorktown Heights, New York, NY, USA
| | - Aritra Bose
- IBM T. J. Watson Research Center, Yorktown Heights, New York, NY, USA
| | | | - Filippo Utro
- IBM T. J. Watson Research Center, Yorktown Heights, New York, NY, USA
| | - Laxmi Parida
- IBM T. J. Watson Research Center, Yorktown Heights, New York, NY, USA
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Guzinski J, Tang Y, Chattaway MA, Dallman TJ, Petrovska L. Development and validation of a random forest algorithm for source attribution of animal and human Salmonella Typhimurium and monophasic variants of S. Typhimurium isolates in England and Wales utilising whole genome sequencing data. Front Microbiol 2024; 14:1254860. [PMID: 38533130 PMCID: PMC10963456 DOI: 10.3389/fmicb.2023.1254860] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2023] [Accepted: 12/22/2023] [Indexed: 03/28/2024] Open
Abstract
Source attribution has traditionally involved combining epidemiological data with different pathogen characterisation methods, including 7-gene multi locus sequence typing (MLST) or serotyping, however, these approaches have limited resolution. In contrast, whole genome sequencing data provide an overview of the whole genome that can be used by attribution algorithms. Here, we applied a random forest (RF) algorithm to predict the primary sources of human clinical Salmonella Typhimurium (S. Typhimurium) and monophasic variants (monophasic S. Typhimurium) isolates. To this end, we utilised single nucleotide polymorphism diversity in the core genome MLST alleles obtained from 1,061 laboratory-confirmed human and animal S. Typhimurium and monophasic S. Typhimurium isolates as inputs into a RF model. The algorithm was used for supervised learning to classify 399 animal S. Typhimurium and monophasic S. Typhimurium isolates into one of eight distinct primary source classes comprising common livestock and pet animal species: cattle, pigs, sheep, other mammals (pets: mostly dogs and horses), broilers, layers, turkeys, and game birds (pheasants, quail, and pigeons). When applied to the training set animal isolates, model accuracy was 0.929 and kappa 0.905, whereas for the test set animal isolates, for which the primary source class information was withheld from the model, the accuracy was 0.779 and kappa 0.700. Subsequently, the model was applied to assign 662 human clinical cases to the eight primary source classes. In the dataset, 60/399 (15.0%) of the animal and 141/662 (21.3%) of the human isolates were associated with a known outbreak of S. Typhimurium definitive type (DT) 104. All but two of the 141 DT104 outbreak linked human isolates were correctly attributed by the model to the primary source classes identified as the origin of the DT104 outbreak. A model that was run without the clonal DT104 animal isolates produced largely congruent outputs (training set accuracy 0.989 and kappa 0.985; test set accuracy 0.781 and kappa 0.663). Overall, our results show that RF offers considerable promise as a suitable methodology for epidemiological tracking and source attribution for foodborne pathogens.
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Affiliation(s)
- Jaromir Guzinski
- Animal and Plant Health Agency, Bacteriology Department, Addlestone, United Kingdom
| | - Yue Tang
- Animal and Plant Health Agency, Bacteriology Department, Addlestone, United Kingdom
| | - Marie Anne Chattaway
- Gastrointestinal Bacteria Reference Unit, UK Health Security Agency, London, United Kingdom
| | - Timothy J. Dallman
- Gastrointestinal Bacteria Reference Unit, UK Health Security Agency, London, United Kingdom
| | - Liljana Petrovska
- Animal and Plant Health Agency, Bacteriology Department, Addlestone, United Kingdom
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Chafai N, Bonizzi L, Botti S, Badaoui B. Emerging applications of machine learning in genomic medicine and healthcare. Crit Rev Clin Lab Sci 2024; 61:140-163. [PMID: 37815417 DOI: 10.1080/10408363.2023.2259466] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2023] [Accepted: 09/12/2023] [Indexed: 10/11/2023]
Abstract
The integration of artificial intelligence technologies has propelled the progress of clinical and genomic medicine in recent years. The significant increase in computing power has facilitated the ability of artificial intelligence models to analyze and extract features from extensive medical data and images, thereby contributing to the advancement of intelligent diagnostic tools. Artificial intelligence (AI) models have been utilized in the field of personalized medicine to integrate clinical data and genomic information of patients. This integration allows for the identification of customized treatment recommendations, ultimately leading to enhanced patient outcomes. Notwithstanding the notable advancements, the application of artificial intelligence (AI) in the field of medicine is impeded by various obstacles such as the limited availability of clinical and genomic data, the diversity of datasets, ethical implications, and the inconclusive interpretation of AI models' results. In this review, a comprehensive evaluation of multiple machine learning algorithms utilized in the fields of clinical and genomic medicine is conducted. Furthermore, we present an overview of the implementation of artificial intelligence (AI) in the fields of clinical medicine, drug discovery, and genomic medicine. Finally, a number of constraints pertaining to the implementation of artificial intelligence within the healthcare industry are examined.
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Affiliation(s)
- Narjice Chafai
- Laboratory of Biodiversity, Ecology, and Genome, Faculty of Sciences, Department of Biology, Mohammed V University in Rabat, Rabat, Morocco
| | - Luigi Bonizzi
- Department of Biomedical, Surgical and Dental Science, University of Milan, Milan, Italy
| | - Sara Botti
- PTP Science Park, Via Einstein - Loc. Cascina Codazza, Lodi, Italy
| | - Bouabid Badaoui
- Laboratory of Biodiversity, Ecology, and Genome, Faculty of Sciences, Department of Biology, Mohammed V University in Rabat, Rabat, Morocco
- African Sustainable Agriculture Research Institute (ASARI), Mohammed VI Polytechnic University (UM6P), Laâyoune, Morocco
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Barash M, McNevin D, Fedorenko V, Giverts P. Machine learning applications in forensic DNA profiling: A critical review. Forensic Sci Int Genet 2024; 69:102994. [PMID: 38086200 DOI: 10.1016/j.fsigen.2023.102994] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2023] [Revised: 11/06/2023] [Accepted: 11/26/2023] [Indexed: 01/29/2024]
Abstract
Machine learning (ML) is a range of powerful computational algorithms capable of generating predictive models via intelligent autonomous analysis of relatively large and often unstructured data. ML has become an integral part of our daily lives with a plethora of applications, including web, business, automotive industry, clinical diagnostics, scientific research, and more recently, forensic science. In the field of forensic DNA, the manual analysis of complex data can be challenging, time-consuming, and error-prone. The integration of novel ML-based methods may aid in streamlining this process while maintaining the high accuracy and reproducibility required for forensic tools. Due to the relative novelty of such applications, the forensic community is largely unaware of ML capabilities and limitations. Furthermore, computer science and ML professionals are often unfamiliar with the forensic science field and its specific requirements. This manuscript offers a brief introduction to the capabilities of machine learning methods and their applications in the context of forensic DNA analysis and offers a critical review of the current literature in this rapidly developing field.
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Affiliation(s)
- Mark Barash
- Department of Justice Studies, San José State University, San Jose, CA, United States; Centre for Forensic Science, School of Mathematical and Physical Sciences, Faculty of Science, University of Technology Sydney, Broadway, Ultimo, NSW 2007, Australia.
| | - Dennis McNevin
- Centre for Forensic Science, School of Mathematical and Physical Sciences, Faculty of Science, University of Technology Sydney, Broadway, Ultimo, NSW 2007, Australia
| | - Vladimir Fedorenko
- The Educational and Scientific Laboratory of Forensic Materials Engineering of the Saratov State University, Russia
| | - Pavel Giverts
- Division of Identification and Forensic Science, Israel Police HQ, Haim Bar-Lev Road, Jerusalem, Israel
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Chen Y, Mancini M, Zhu X, Akata Z. Semi-Supervised and Unsupervised Deep Visual Learning: A Survey. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2024; 46:1327-1347. [PMID: 36006881 DOI: 10.1109/tpami.2022.3201576] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
State-of-the-art deep learning models are often trained with a large amount of costly labeled training data. However, requiring exhaustive manual annotations may degrade the model's generalizability in the limited-label regime.Semi-supervised learning and unsupervised learning offer promising paradigms to learn from an abundance of unlabeled visual data. Recent progress in these paradigms has indicated the strong benefits of leveraging unlabeled data to improve model generalization and provide better model initialization. In this survey, we review the recent advanced deep learning algorithms on semi-supervised learning (SSL) and unsupervised learning (UL) for visual recognition from a unified perspective. To offer a holistic understanding of the state-of-the-art in these areas, we propose a unified taxonomy. We categorize existing representative SSL and UL with comprehensive and insightful analysis to highlight their design rationales in different learning scenarios and applications in different computer vision tasks. Lastly, we discuss the emerging trends and open challenges in SSL and UL to shed light on future critical research directions.
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Fang C, Dziedzic A, Zhang L, Oliva L, Verma A, Razak F, Papernot N, Wang B. Decentralised, collaborative, and privacy-preserving machine learning for multi-hospital data. EBioMedicine 2024; 101:105006. [PMID: 38377795 PMCID: PMC10884342 DOI: 10.1016/j.ebiom.2024.105006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2023] [Revised: 01/26/2024] [Accepted: 01/28/2024] [Indexed: 02/22/2024] Open
Abstract
BACKGROUND Machine Learning (ML) has demonstrated its great potential on medical data analysis. Large datasets collected from diverse sources and settings are essential for ML models in healthcare to achieve better accuracy and generalizability. Sharing data across different healthcare institutions or jurisdictions is challenging because of complex and varying privacy and regulatory requirements. Hence, it is hard but crucial to allow multiple parties to collaboratively train an ML model leveraging the private datasets available at each party without the need for direct sharing of those datasets or compromising the privacy of the datasets through collaboration. METHODS In this paper, we address this challenge by proposing Decentralized, Collaborative, and Privacy-preserving ML for Multi-Hospital Data (DeCaPH). This framework offers the following key benefits: (1) it allows different parties to collaboratively train an ML model without transferring their private datasets (i.e., no data centralization); (2) it safeguards patients' privacy by limiting the potential privacy leakage arising from any contents shared across the parties during the training process; and (3) it facilitates the ML model training without relying on a centralized party/server. FINDINGS We demonstrate the generalizability and power of DeCaPH on three distinct tasks using real-world distributed medical datasets: patient mortality prediction using electronic health records, cell-type classification using single-cell human genomes, and pathology identification using chest radiology images. The ML models trained with DeCaPH framework have less than 3.2% drop in model performance comparing to those trained by the non-privacy-preserving collaborative framework. Meanwhile, the average vulnerability to privacy attacks of the models trained with DeCaPH decreased by up to 16%. In addition, models trained with our DeCaPH framework achieve better performance than those models trained solely with the private datasets from individual parties without collaboration and those trained with the previous privacy-preserving collaborative training framework under the same privacy guarantee by up to 70% and 18.2% respectively. INTERPRETATION We demonstrate that the ML models trained with DeCaPH framework have an improved utility-privacy trade-off, showing DeCaPH enables the models to have good performance while preserving the privacy of the training data points. In addition, the ML models trained with DeCaPH framework in general outperform those trained solely with the private datasets from individual parties, showing that DeCaPH enhances the model generalizability. FUNDING This work was supported by the Natural Sciences and Engineering Research Council of Canada (NSERC, RGPIN-2020-06189 and DGECR-2020-00294), Canadian Institute for Advanced Research (CIFAR) AI Catalyst Grants, CIFAR AI Chair programs, Temerty Professor of AI Research and Education in Medicine, University of Toronto, Amazon, Apple, DARPA through the GARD project, Intel, Meta, the Ontario Early Researcher Award, and the Sloan Foundation. Resources used in preparing this research were provided, in part, by the Province of Ontario, the Government of Canada through CIFAR, and companies sponsoring the Vector Institute.
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Affiliation(s)
- Congyu Fang
- Department of Computer Science, University of Toronto, Canada; Peter Munk Cardiac Centre, University Health Network, Canada; Vector Institute, Toronto, Canada
| | - Adam Dziedzic
- Vector Institute, Toronto, Canada; CISPA Helmholtz Center for Information Security, Germany; Department of Electrical and Computer Engineering, University of Toronto, Canada
| | - Lin Zhang
- Peter Munk Cardiac Centre, University Health Network, Canada; Simon Fraser University, Canada
| | - Laura Oliva
- Peter Munk Cardiac Centre, University Health Network, Canada
| | - Amol Verma
- St. Michael's Hospital, Unity Health Toronto, Canada; Department of Medicine, University of Toronto, Canada; Institute of Health Policy, Management and Evaluation, University of Toronto, Canada
| | - Fahad Razak
- St. Michael's Hospital, Unity Health Toronto, Canada; Department of Medicine, University of Toronto, Canada; Institute of Health Policy, Management and Evaluation, University of Toronto, Canada
| | - Nicolas Papernot
- Department of Computer Science, University of Toronto, Canada; Vector Institute, Toronto, Canada; Department of Electrical and Computer Engineering, University of Toronto, Canada.
| | - Bo Wang
- Department of Computer Science, University of Toronto, Canada; Peter Munk Cardiac Centre, University Health Network, Canada; Vector Institute, Toronto, Canada; Department of Laboratory Medicine and Pathobiology, Temerty Faculty of Medicine, University of Toronto, Canada.
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Bai Y, Lin H, Wang C, Wang Q, Qu J. Digitalizing river aquatic ecosystems. J Environ Sci (China) 2024; 137:677-680. [PMID: 37980050 DOI: 10.1016/j.jes.2023.03.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2022] [Revised: 03/07/2023] [Accepted: 03/08/2023] [Indexed: 11/20/2023]
Abstract
Traditional river health assessment relies on limited water quality indices and representative organism activity, but does not comprehensively obtain biotic and abiotic information of the ecosystem. Here, we propose a new approach to evaluate the ecological and health risks of river aquatic ecosystems. First, detailed physicochemical and biological characterization of a river ecosystem can be obtained through pollutant determination (especially emerging pollutants) and DNA/RNA sequencing. Second, supervised machine learning can be applied to perform classification analysis of characterization data and ascertain river ecosystem ecology and health. Our proposed methodology transforms river ecosystem health assessment and can be applied in river management.
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Affiliation(s)
- Yaohui Bai
- Key Laboratory of Drinking Water Science and Technology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China.
| | - Hui Lin
- Key Laboratory of Drinking Water Science and Technology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Chenchen Wang
- Key Laboratory of Drinking Water Science and Technology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China; School of Environmental and Municipal Engineering, Tianjin Chengjian University, Tianjin 300384, China
| | - Qiaojuan Wang
- Key Laboratory of Drinking Water Science and Technology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Jiuhui Qu
- Key Laboratory of Drinking Water Science and Technology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China; Center for Water and Ecology, Tsinghua University, Beijing 100084, China.
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Hazan JM, Amador R, Ali-Nasser T, Lahav T, Shotan SR, Steinberg M, Cohen Z, Aran D, Meiri D, Assaraf YG, Guigó R, Bester AC. Integration of transcription regulation and functional genomic data reveals lncRNA SNHG6's role in hematopoietic differentiation and leukemia. J Biomed Sci 2024; 31:27. [PMID: 38419051 PMCID: PMC10900714 DOI: 10.1186/s12929-024-01015-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2023] [Accepted: 02/22/2024] [Indexed: 03/02/2024] Open
Abstract
BACKGROUND Long non-coding RNAs (lncRNAs) are pivotal players in cellular processes, and their unique cell-type specific expression patterns render them attractive biomarkers and therapeutic targets. Yet, the functional roles of most lncRNAs remain enigmatic. To address the need to identify new druggable lncRNAs, we developed a comprehensive approach integrating transcription factor binding data with other genetic features to generate a machine learning model, which we have called INFLAMeR (Identifying Novel Functional LncRNAs with Advanced Machine Learning Resources). METHODS INFLAMeR was trained on high-throughput CRISPR interference (CRISPRi) screens across seven cell lines, and the algorithm was based on 71 genetic features. To validate the predictions, we selected candidate lncRNAs in the human K562 leukemia cell line and determined the impact of their knockdown (KD) on cell proliferation and chemotherapeutic drug response. We further performed transcriptomic analysis for candidate genes. Based on these findings, we assessed the lncRNA small nucleolar RNA host gene 6 (SNHG6) for its role in myeloid differentiation. Finally, we established a mouse K562 leukemia xenograft model to determine whether SNHG6 KD attenuates tumor growth in vivo. RESULTS The INFLAMeR model successfully reconstituted CRISPRi screening data and predicted functional lncRNAs that were previously overlooked. Intensive cell-based and transcriptomic validation of nearly fifty genes in K562 revealed cell type-specific functionality for 85% of the predicted lncRNAs. In this respect, our cell-based and transcriptomic analyses predicted a role for SNHG6 in hematopoiesis and leukemia. Consistent with its predicted role in hematopoietic differentiation, SNHG6 transcription is regulated by hematopoiesis-associated transcription factors. SNHG6 KD reduced the proliferation of leukemia cells and sensitized them to differentiation. Treatment of K562 leukemic cells with hemin and PMA, respectively, demonstrated that SNHG6 inhibits red blood cell differentiation but strongly promotes megakaryocyte differentiation. Using a xenograft mouse model, we demonstrate that SNHG6 KD attenuated tumor growth in vivo. CONCLUSIONS Our approach not only improved the identification and characterization of functional lncRNAs through genomic approaches in a cell type-specific manner, but also identified new lncRNAs with roles in hematopoiesis and leukemia. Such approaches can be readily applied to identify novel targets for precision medicine.
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Affiliation(s)
- Joshua M Hazan
- Department of Biology, Technion-Israel Institute of Technology, 3200003, Haifa, Israel
| | - Raziel Amador
- Centre for Genomic Regulation (CRG), Doctor Aiguader 88, 08003, Barcelona, Catalonia, Spain
- Universitat de Barcelona (UB), Barcelona, Catalonia, Spain
| | - Tahleel Ali-Nasser
- Department of Biology, Technion-Israel Institute of Technology, 3200003, Haifa, Israel
| | - Tamar Lahav
- Department of Biology, Technion-Israel Institute of Technology, 3200003, Haifa, Israel
| | - Stav Roni Shotan
- Department of Biology, Technion-Israel Institute of Technology, 3200003, Haifa, Israel
| | - Miryam Steinberg
- Department of Biology, Technion-Israel Institute of Technology, 3200003, Haifa, Israel
| | - Ziv Cohen
- Department of Biology, Technion-Israel Institute of Technology, 3200003, Haifa, Israel
- The Taub Faculty of Computer Science, Technion-Israel Institute of Technology, 3200003, Haifa, Israel
| | - Dvir Aran
- Department of Biology, Technion-Israel Institute of Technology, 3200003, Haifa, Israel
- The Taub Faculty of Computer Science, Technion-Israel Institute of Technology, 3200003, Haifa, Israel
| | - David Meiri
- Department of Biology, Technion-Israel Institute of Technology, 3200003, Haifa, Israel
| | - Yehuda G Assaraf
- The Fred Wyszkowski Cancer Research Laboratory, Department of Biology, Technion-Israel Institute of Technology, 3200003, Haifa, Israel
| | - Roderic Guigó
- Centre for Genomic Regulation (CRG), Doctor Aiguader 88, 08003, Barcelona, Catalonia, Spain
- Universitat Pompeu Fabra (UPF), Barcelona, Catalonia, Spain
| | - Assaf C Bester
- Department of Biology, Technion-Israel Institute of Technology, 3200003, Haifa, Israel.
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Rahit KMTH, Avramovic V, Chong JX, Tarailo-Graovac M. GPAD: a natural language processing-based application to extract the gene-disease association discovery information from OMIM. BMC Bioinformatics 2024; 25:84. [PMID: 38413851 PMCID: PMC10898068 DOI: 10.1186/s12859-024-05693-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2023] [Accepted: 02/09/2024] [Indexed: 02/29/2024] Open
Abstract
BACKGROUND Thousands of genes have been associated with different Mendelian conditions. One of the valuable sources to track these gene-disease associations (GDAs) is the Online Mendelian Inheritance in Man (OMIM) database. However, most of the information in OMIM is textual, and heterogeneous (e.g. summarized by different experts), which complicates automated reading and understanding of the data. Here, we used Natural Language Processing (NLP) to make a tool (Gene-Phenotype Association Discovery (GPAD)) that could syntactically process OMIM text and extract the data of interest. RESULTS GPAD applies a series of language-based techniques to the text obtained from OMIM API to extract GDA discovery-related information. GPAD can inform when a particular gene was associated with a specific phenotype, as well as the type of validation-whether through model organisms or cohort-based patient-matching approaches-for such an association. GPAD extracted data was validated with published reports and was compared with large language model. Utilizing GPAD's extracted data, we analysed trends in GDA discoveries, noting a significant increase in their rate after the introduction of exome sequencing, rising from an average of about 150-250 discoveries each year. Contrary to hopes of resolving most GDAs for Mendelian disorders by now, our data indicate a substantial decline in discovery rates over the past five years (2017-2022). This decline appears to be linked to the increasing necessity for larger cohorts to substantiate GDAs. The rising use of zebrafish and Drosophila as model organisms in providing evidential support for GDAs is also observed. CONCLUSIONS GPAD's real-time analyzing capacity offers an up-to-date view of GDA discovery and could help in planning and managing the research strategies. In future, this solution can be extended or modified to capture other information in OMIM and scientific literature.
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Affiliation(s)
- K M Tahsin Hassan Rahit
- Departments of Biochemistry, Molecular Biology and Medical Genetics, Cumming School of Medicine, University of Calgary, Calgary, AB, T2N 4N1, Canada
- Alberta Children's Hospital Research Institute, University of Calgary, Calgary, AB, T2N 4N1, Canada
| | - Vladimir Avramovic
- Departments of Biochemistry, Molecular Biology and Medical Genetics, Cumming School of Medicine, University of Calgary, Calgary, AB, T2N 4N1, Canada
- Alberta Children's Hospital Research Institute, University of Calgary, Calgary, AB, T2N 4N1, Canada
| | - Jessica X Chong
- Division of Genetic Medicine, Department of Pediatrics, University of Washington, Seattle, WA, 98195, USA
- Brotman-Baty Institute, Seattle, WA, 98195, USA
| | - Maja Tarailo-Graovac
- Departments of Biochemistry, Molecular Biology and Medical Genetics, Cumming School of Medicine, University of Calgary, Calgary, AB, T2N 4N1, Canada.
- Alberta Children's Hospital Research Institute, University of Calgary, Calgary, AB, T2N 4N1, Canada.
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Li G, Li C, Wang C, Wang Z. Suboptimal capability of individual machine learning algorithms in modeling small-scale imbalanced clinical data of local hospital. PLoS One 2024; 19:e0298328. [PMID: 38394317 PMCID: PMC10890755 DOI: 10.1371/journal.pone.0298328] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2023] [Accepted: 01/22/2024] [Indexed: 02/25/2024] Open
Abstract
In recent years, artificial intelligence (AI) has shown promising applications in various scientific domains, including biochemical analysis research. However, the effectiveness of AI in modeling small-scale, imbalanced datasets remains an open question in such fields. This study explores the capabilities of eight basic AI algorithms, including ridge regression, logistic regression, random forest regression, and others, in modeling a small, imbalanced clinical dataset (total n = 387, class 0 = 27, class 1 = 360) related to the records of the biochemical blood tests from the patients with multiple wasp stings (MWS). Through rigorous evaluation using k-fold cross-validation and comprehensive scoring, we found that none of the models could effectively model the data. Even after fine-tuning the hyperparameters of the best-performing models, the results remained below acceptable thresholds. The study highlights the challenges of applying AI to small-scale datasets with imbalanced groups in biochemical or clinical research and emphasizes the need for novel algorithms tailored to small-scale data. The findings also call for further exploration into techniques such as transfer learning and data augmentation, and they underline the importance of understanding the minimum dataset scale required for effective AI modeling in biochemical contexts.
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Affiliation(s)
- Gang Li
- Department of ICU, 3201 Hospital, Hanzhong, Shaanxi, China
| | - Chenbi Li
- Department of ICU, 3201 Hospital, Hanzhong, Shaanxi, China
| | - Chengli Wang
- Department of ICU, 3201 Hospital, Hanzhong, Shaanxi, China
| | - Zeheng Wang
- Data61, CSIRO, Clayton, VIC, Australia
- Manufacturing, CSIRO, West Lindfield, NSW, Australia
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Fong WJ, Tan HM, Garg R, Teh AL, Pan H, Gupta V, Krishna B, Chen ZH, Purwanto NY, Yap F, Tan KH, Chan KYJ, Chan SY, Goh N, Rane N, Tan ESE, Jiang Y, Han M, Meaney M, Wang D, Keppo J, Tan GCY. Comparing feature selection and machine learning approaches for predicting CYP2D6 methylation from genetic variation. Front Neuroinform 2024; 17:1244336. [PMID: 38449836 PMCID: PMC10915285 DOI: 10.3389/fninf.2023.1244336] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2023] [Accepted: 10/18/2023] [Indexed: 03/08/2024] Open
Abstract
Introduction Pharmacogenetics currently supports clinical decision-making on the basis of a limited number of variants in a few genes and may benefit paediatric prescribing where there is a need for more precise dosing. Integrating genomic information such as methylation into pharmacogenetic models holds the potential to improve their accuracy and consequently prescribing decisions. Cytochrome P450 2D6 (CYP2D6) is a highly polymorphic gene conventionally associated with the metabolism of commonly used drugs and endogenous substrates. We thus sought to predict epigenetic loci from single nucleotide polymorphisms (SNPs) related to CYP2D6 in children from the GUSTO cohort. Methods Buffy coat DNA methylation was quantified using the Illumina Infinium Methylation EPIC beadchip. CpG sites associated with CYP2D6 were used as outcome variables in Linear Regression, Elastic Net and XGBoost models. We compared feature selection of SNPs from GWAS mQTLs, GTEx eQTLs and SNPs within 2 MB of the CYP2D6 gene and the impact of adding demographic data. The samples were split into training (75%) sets and test (25%) sets for validation. In Elastic Net model and XGBoost models, optimal hyperparameter search was done using 10-fold cross validation. Root Mean Square Error and R-squared values were obtained to investigate each models' performance. When GWAS was performed to determine SNPs associated with CpG sites, a total of 15 SNPs were identified where several SNPs appeared to influence multiple CpG sites. Results Overall, Elastic Net models of genetic features appeared to perform marginally better than heritability estimates and substantially better than Linear Regression and XGBoost models. The addition of nongenetic features appeared to improve performance for some but not all feature sets and probes. The best feature set and Machine Learning (ML) approach differed substantially between CpG sites and a number of top variables were identified for each model. Discussion The development of SNP-based prediction models for CYP2D6 CpG methylation in Singaporean children of varying ethnicities in this study has clinical application. With further validation, they may add to the set of tools available to improve precision medicine and pharmacogenetics-based dosing.
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Affiliation(s)
- Wei Jing Fong
- Computational Biology, National University of Singapore, Singapore, Singapore
| | - Hong Ming Tan
- Computational Biology, National University of Singapore, Singapore, Singapore
| | - Rishabh Garg
- Computational Biology, National University of Singapore, Singapore, Singapore
| | - Ai Ling Teh
- Singapore Institute for Clinical Sciences (SICS), Agency for Science, Technology and Research (A*STAR), Singapore, Singapore
- Bioinformatics Institute (BII), Agency for Science, Technology and Research (A*STAR), Singapore, Singapore
| | - Hong Pan
- Singapore Institute for Clinical Sciences (SICS), Agency for Science, Technology and Research (A*STAR), Singapore, Singapore
| | - Varsha Gupta
- Singapore Institute for Clinical Sciences (SICS), Agency for Science, Technology and Research (A*STAR), Singapore, Singapore
- Bioinformatics Institute (BII), Agency for Science, Technology and Research (A*STAR), Singapore, Singapore
| | - Bernadus Krishna
- Computational Biology, National University of Singapore, Singapore, Singapore
| | - Zou Hui Chen
- Computational Biology, National University of Singapore, Singapore, Singapore
| | | | - Fabian Yap
- KK Women's and Children's Hospital, Singapore, Singapore
| | - Kok Hian Tan
- KK Women's and Children's Hospital, Singapore, Singapore
- Duke NUS Medical School, Singapore, Singapore
| | - Kok Yen Jerry Chan
- KK Women's and Children's Hospital, Singapore, Singapore
- Duke NUS Medical School, Singapore, Singapore
| | - Shiao-Yng Chan
- Singapore Institute for Clinical Sciences (SICS), Agency for Science, Technology and Research (A*STAR), Singapore, Singapore
- National University Hospital, Singapore, Singapore
| | | | - Nikita Rane
- Institute of Mental Health,Singapore, Singapore
| | | | | | - Mei Han
- Computational Biology, National University of Singapore, Singapore, Singapore
| | - Michael Meaney
- Singapore Institute for Clinical Sciences (SICS), Agency for Science, Technology and Research (A*STAR), Singapore, Singapore
| | - Dennis Wang
- Singapore Institute for Clinical Sciences (SICS), Agency for Science, Technology and Research (A*STAR), Singapore, Singapore
- National Heart and Lung Institute, Imperial College London, London, United Kingdom
| | - Jussi Keppo
- Computational Biology, National University of Singapore, Singapore, Singapore
| | - Geoffrey Chern-Yee Tan
- Computational Biology, National University of Singapore, Singapore, Singapore
- Institute of Mental Health,Singapore, Singapore
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Lv Q, Liu Y, Sun Y, Wu M. Insight into deep learning for glioma IDH medical image analysis: A systematic review. Medicine (Baltimore) 2024; 103:e37150. [PMID: 38363910 PMCID: PMC10869095 DOI: 10.1097/md.0000000000037150] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/18/2023] [Accepted: 01/11/2024] [Indexed: 02/18/2024] Open
Abstract
BACKGROUND Deep learning techniques explain the enormous potential of medical image analysis, particularly in digital pathology. Concurrently, molecular markers have gained increasing significance over the past decade in the context of glioma patients, providing novel insights into diagnosis and more personalized treatment options. Deep learning combined with imaging and molecular analysis enables more accurate prognostication of patients, more accurate treatment plan proposals, and accurate biomarker (IDH) prediction for gliomas. This systematic study examines the development of deep learning techniques for IDH prediction using histopathology images, spanning the period from 2019 to 2023. METHOD The study adhered to the PRISMA reporting requirements, and databases including PubMed, Google Scholar, Google Search, and preprint repositories (such as arXiv) were systematically queried for pertinent literature spanning the period from 2019 to the 30th of 2023. Search phrases related to deep learning, digital pathology, glioma, and IDH were collaboratively utilized. RESULTS Fifteen papers meeting the inclusion criteria were included in the analysis. These criteria specifically encompassed studies utilizing deep learning for the analysis of hematoxylin and eosin images to determine the IDH status in patients with gliomas. CONCLUSIONS When predicting the status of IDH, the classifier built on digital pathological images demonstrates exceptional performance. The study's predictive effectiveness is enhanced with the utilization of the appropriate deep learning model. However, external verification is necessary to showcase their resilience and universality. Larger sample sizes and multicenter samples are necessary for more comprehensive research to evaluate performance and confirm clinical advantages.
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Affiliation(s)
- Qingqing Lv
- Hunan Cancer Hospital, The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha 410008, Hunan, China
- The Key Laboratory of Carcinogenesis of the Chinese Ministry of Health, The Key Laboratory of Carcinogenesis and Cancer Invasion of the Chinese Ministry of Education, Cancer Research Institute, Central South University, Changsha, 410078, Hunan, China
| | - Yihao Liu
- Hunan Cancer Hospital, The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha 410008, Hunan, China
- The Key Laboratory of Carcinogenesis of the Chinese Ministry of Health, The Key Laboratory of Carcinogenesis and Cancer Invasion of the Chinese Ministry of Education, Cancer Research Institute, Central South University, Changsha, 410078, Hunan, China
| | - Yingnan Sun
- Hunan Cancer Hospital, The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha 410008, Hunan, China
| | - Minghua Wu
- Hunan Cancer Hospital, The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha 410008, Hunan, China
- The Key Laboratory of Carcinogenesis of the Chinese Ministry of Health, The Key Laboratory of Carcinogenesis and Cancer Invasion of the Chinese Ministry of Education, Cancer Research Institute, Central South University, Changsha, 410078, Hunan, China
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Shahjahan, Dey JK, Dey SK. Translational bioinformatics approach to combat cardiovascular disease and cancers. ADVANCES IN PROTEIN CHEMISTRY AND STRUCTURAL BIOLOGY 2024; 139:221-261. [PMID: 38448136 DOI: 10.1016/bs.apcsb.2023.11.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/08/2024]
Abstract
Bioinformatics is an interconnected subject of science dealing with diverse fields including biology, chemistry, physics, statistics, mathematics, and computer science as the key fields to answer complicated physiological problems. Key intention of bioinformatics is to store, analyze, organize, and retrieve essential information about genome, proteome, transcriptome, metabolome, as well as organisms to investigate the biological system along with its dynamics, if any. The outcome of bioinformatics depends on the type, quantity, and quality of the raw data provided and the algorithm employed to analyze the same. Despite several approved medicines available, cardiovascular disorders (CVDs) and cancers comprises of the two leading causes of human deaths. Understanding the unknown facts of both these non-communicable disorders is inevitable to discover new pathways, find new drug targets, and eventually newer drugs to combat them successfully. Since, all these goals involve complex investigation and handling of various types of macro- and small- molecules of the human body, bioinformatics plays a key role in such processes. Results from such investigation has direct human application and thus we call this filed as translational bioinformatics. Current book chapter thus deals with diverse scope and applications of this translational bioinformatics to find cure, diagnosis, and understanding the mechanisms of CVDs and cancers. Developing complex yet small or long algorithms to address such problems is very common in translational bioinformatics. Structure-based drug discovery or AI-guided invention of novel antibodies that too with super-high accuracy, speed, and involvement of considerably low amount of investment are some of the astonishing features of the translational bioinformatics and its applications in the fields of CVDs and cancers.
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Affiliation(s)
- Shahjahan
- Laboratory for Structural Biology of Membrane Proteins, Dr. B.R. Ambedkar Center for Biomedical Research, University of Delhi, Delhi, India
| | - Joy Kumar Dey
- Central Council for Research in Homoeopathy, Ministry of Ayush, Govt. of India, New Delhi, Delhi, India
| | - Sanjay Kumar Dey
- Laboratory for Structural Biology of Membrane Proteins, Dr. B.R. Ambedkar Center for Biomedical Research, University of Delhi, Delhi, India.
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Hassan J, Saeed SM, Deka L, Uddin MJ, Das DB. Applications of Machine Learning (ML) and Mathematical Modeling (MM) in Healthcare with Special Focus on Cancer Prognosis and Anticancer Therapy: Current Status and Challenges. Pharmaceutics 2024; 16:260. [PMID: 38399314 PMCID: PMC10892549 DOI: 10.3390/pharmaceutics16020260] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2023] [Revised: 01/29/2024] [Accepted: 02/07/2024] [Indexed: 02/25/2024] Open
Abstract
The use of data-driven high-throughput analytical techniques, which has given rise to computational oncology, is undisputed. The widespread use of machine learning (ML) and mathematical modeling (MM)-based techniques is widely acknowledged. These two approaches have fueled the advancement in cancer research and eventually led to the uptake of telemedicine in cancer care. For diagnostic, prognostic, and treatment purposes concerning different types of cancer research, vast databases of varied information with manifold dimensions are required, and indeed, all this information can only be managed by an automated system developed utilizing ML and MM. In addition, MM is being used to probe the relationship between the pharmacokinetics and pharmacodynamics (PK/PD interactions) of anti-cancer substances to improve cancer treatment, and also to refine the quality of existing treatment models by being incorporated at all steps of research and development related to cancer and in routine patient care. This review will serve as a consolidation of the advancement and benefits of ML and MM techniques with a special focus on the area of cancer prognosis and anticancer therapy, leading to the identification of challenges (data quantity, ethical consideration, and data privacy) which are yet to be fully addressed in current studies.
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Affiliation(s)
- Jasmin Hassan
- Drug Delivery & Therapeutics Lab, Dhaka 1212, Bangladesh; (J.H.); (S.M.S.)
| | | | - Lipika Deka
- Faculty of Computing, Engineering and Media, De Montfort University, Leicester LE1 9BH, UK;
| | - Md Jasim Uddin
- Department of Pharmaceutical Technology, Faculty of Pharmacy, Universiti Malaya, Kuala Lumpur 50603, Malaysia
| | - Diganta B. Das
- Department of Chemical Engineering, Loughborough University, Loughborough LE11 3TU, UK
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Wong EY, Chu TN, Ladi-Seyedian SS. Genomics and Artificial Intelligence: Prostate Cancer. Urol Clin North Am 2024; 51:27-33. [PMID: 37945100 DOI: 10.1016/j.ucl.2023.06.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2023]
Abstract
Artificial intelligence (AI) is revolutionizing prostate cancer genomics research. By leveraging machine learning and deep learning algorithms, researchers can rapidly analyze vast genomic datasets to identify patterns and correlations that may be missed by traditional methods. These AI-driven insights can lead to the discovery of novel biomarkers, enhance the accuracy of diagnosis, and predict disease progression and treatment response. As such, AI is becoming an indispensable tool in the pursuit of personalized medicine for prostate cancer.
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Affiliation(s)
- Elyssa Y Wong
- Catherine & Joseph Aresty Department of Urology, Center for Robotic Simulation & Education, Norris Comprehensive Cancer Center, University of Southern California, Los Angeles, CA, USA
| | - Timothy N Chu
- Catherine & Joseph Aresty Department of Urology, Center for Robotic Simulation & Education, Norris Comprehensive Cancer Center, University of Southern California, Los Angeles, CA, USA
| | - Seyedeh-Sanam Ladi-Seyedian
- Catherine & Joseph Aresty Department of Urology, Center for Robotic Simulation & Education, Norris Comprehensive Cancer Center, University of Southern California, Los Angeles, CA, USA.
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