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Abdelfattah F, Schulz H, Wehland M, Corydon TJ, Sahana J, Kraus A, Krüger M, González-Torres LF, Cortés-Sánchez JL, Wise PM, Mushunuri A, Hemmersbach R, Liemersdorf C, Infanger M, Grimm D. Omics Studies of Specialized Cells and Stem Cells under Microgravity Conditions. Int J Mol Sci 2024; 25:10014. [PMID: 39337501 PMCID: PMC11431953 DOI: 10.3390/ijms251810014] [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: 08/20/2024] [Revised: 09/06/2024] [Accepted: 09/15/2024] [Indexed: 09/30/2024] Open
Abstract
The primary objective of omics in space with focus on the human organism is to characterize and quantify biological factors that alter structure, morphology, function, and dynamics of human cells exposed to microgravity. This review discusses exciting data regarding genomics, transcriptomics, epigenomics, metabolomics, and proteomics of human cells and individuals in space, as well as cells cultured under simulated microgravity. The NASA Twins Study significantly heightened interest in applying omics technologies and bioinformatics in space and terrestrial environments. Here, we present the available publications in this field with a focus on specialized cells and stem cells exposed to real and simulated microgravity conditions. We summarize current knowledge of the following topics: (i) omics studies on stem cells, (ii) omics studies on benign specialized different cell types of the human organism, (iii) discussing the advantages of this knowledge for space commercialization and exploration, and (iv) summarizing the emerging opportunities for translational regenerative medicine for space travelers and human patients on Earth.
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Affiliation(s)
- Fatima Abdelfattah
- Department of Microgravity and Translational Regenerative Medicine, Otto von Guericke University, 39106 Magdeburg, Germany; (F.A.); (H.S.); (M.W.); (A.K.); (M.K.); (L.F.G.-T.); (J.L.C.-S.); (P.M.W.); (A.M.); (M.I.)
| | - Herbert Schulz
- Department of Microgravity and Translational Regenerative Medicine, Otto von Guericke University, 39106 Magdeburg, Germany; (F.A.); (H.S.); (M.W.); (A.K.); (M.K.); (L.F.G.-T.); (J.L.C.-S.); (P.M.W.); (A.M.); (M.I.)
- Research Group “Magdeburger Arbeitsgemeinschaft für Forschung unter Raumfahrt- und Schwerelosigkeitsbedingungen” (MARS), Otto von Guericke University, 39106 Magdeburg, Germany
| | - Markus Wehland
- Department of Microgravity and Translational Regenerative Medicine, Otto von Guericke University, 39106 Magdeburg, Germany; (F.A.); (H.S.); (M.W.); (A.K.); (M.K.); (L.F.G.-T.); (J.L.C.-S.); (P.M.W.); (A.M.); (M.I.)
- Research Group “Magdeburger Arbeitsgemeinschaft für Forschung unter Raumfahrt- und Schwerelosigkeitsbedingungen” (MARS), Otto von Guericke University, 39106 Magdeburg, Germany
| | - Thomas J. Corydon
- Department of Biomedicine, Aarhus University, 8000 Aarhus, Denmark; (T.J.C.); (J.S.)
- Department of Ophthalmology, Aarhus University Hospital, 8200 Aarhus, Denmark
| | - Jayashree Sahana
- Department of Biomedicine, Aarhus University, 8000 Aarhus, Denmark; (T.J.C.); (J.S.)
| | - Armin Kraus
- Department of Microgravity and Translational Regenerative Medicine, Otto von Guericke University, 39106 Magdeburg, Germany; (F.A.); (H.S.); (M.W.); (A.K.); (M.K.); (L.F.G.-T.); (J.L.C.-S.); (P.M.W.); (A.M.); (M.I.)
- Research Group “Magdeburger Arbeitsgemeinschaft für Forschung unter Raumfahrt- und Schwerelosigkeitsbedingungen” (MARS), Otto von Guericke University, 39106 Magdeburg, Germany
| | - Marcus Krüger
- Department of Microgravity and Translational Regenerative Medicine, Otto von Guericke University, 39106 Magdeburg, Germany; (F.A.); (H.S.); (M.W.); (A.K.); (M.K.); (L.F.G.-T.); (J.L.C.-S.); (P.M.W.); (A.M.); (M.I.)
- Research Group “Magdeburger Arbeitsgemeinschaft für Forschung unter Raumfahrt- und Schwerelosigkeitsbedingungen” (MARS), Otto von Guericke University, 39106 Magdeburg, Germany
| | - Luis Fernando González-Torres
- Department of Microgravity and Translational Regenerative Medicine, Otto von Guericke University, 39106 Magdeburg, Germany; (F.A.); (H.S.); (M.W.); (A.K.); (M.K.); (L.F.G.-T.); (J.L.C.-S.); (P.M.W.); (A.M.); (M.I.)
| | - José Luis Cortés-Sánchez
- Department of Microgravity and Translational Regenerative Medicine, Otto von Guericke University, 39106 Magdeburg, Germany; (F.A.); (H.S.); (M.W.); (A.K.); (M.K.); (L.F.G.-T.); (J.L.C.-S.); (P.M.W.); (A.M.); (M.I.)
| | - Petra M. Wise
- Department of Microgravity and Translational Regenerative Medicine, Otto von Guericke University, 39106 Magdeburg, Germany; (F.A.); (H.S.); (M.W.); (A.K.); (M.K.); (L.F.G.-T.); (J.L.C.-S.); (P.M.W.); (A.M.); (M.I.)
- The Saban Research Institute, Children’s Hospital Los Angeles, University of Southern California, 4650 Sunset Blvd, Los Angeles, CA 90027, USA
| | - Ashwini Mushunuri
- Department of Microgravity and Translational Regenerative Medicine, Otto von Guericke University, 39106 Magdeburg, Germany; (F.A.); (H.S.); (M.W.); (A.K.); (M.K.); (L.F.G.-T.); (J.L.C.-S.); (P.M.W.); (A.M.); (M.I.)
| | - Ruth Hemmersbach
- Department of Applied Aerospace Biology, Institute of Aerospace Medicine, German Aerospace Center (DLR), 51147 Cologne, Germany; (R.H.); (C.L.)
| | - Christian Liemersdorf
- Department of Applied Aerospace Biology, Institute of Aerospace Medicine, German Aerospace Center (DLR), 51147 Cologne, Germany; (R.H.); (C.L.)
| | - Manfred Infanger
- Department of Microgravity and Translational Regenerative Medicine, Otto von Guericke University, 39106 Magdeburg, Germany; (F.A.); (H.S.); (M.W.); (A.K.); (M.K.); (L.F.G.-T.); (J.L.C.-S.); (P.M.W.); (A.M.); (M.I.)
- Research Group “Magdeburger Arbeitsgemeinschaft für Forschung unter Raumfahrt- und Schwerelosigkeitsbedingungen” (MARS), Otto von Guericke University, 39106 Magdeburg, Germany
| | - Daniela Grimm
- Department of Microgravity and Translational Regenerative Medicine, Otto von Guericke University, 39106 Magdeburg, Germany; (F.A.); (H.S.); (M.W.); (A.K.); (M.K.); (L.F.G.-T.); (J.L.C.-S.); (P.M.W.); (A.M.); (M.I.)
- Research Group “Magdeburger Arbeitsgemeinschaft für Forschung unter Raumfahrt- und Schwerelosigkeitsbedingungen” (MARS), Otto von Guericke University, 39106 Magdeburg, Germany
- Department of Biomedicine, Aarhus University, 8000 Aarhus, Denmark; (T.J.C.); (J.S.)
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Pahud de Mortanges A, Luo H, Shu SZ, Kamath A, Suter Y, Shelan M, Pöllinger A, Reyes M. Orchestrating explainable artificial intelligence for multimodal and longitudinal data in medical imaging. NPJ Digit Med 2024; 7:195. [PMID: 39039248 PMCID: PMC11263688 DOI: 10.1038/s41746-024-01190-w] [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: 09/16/2023] [Accepted: 07/15/2024] [Indexed: 07/24/2024] Open
Abstract
Explainable artificial intelligence (XAI) has experienced a vast increase in recognition over the last few years. While the technical developments are manifold, less focus has been placed on the clinical applicability and usability of systems. Moreover, not much attention has been given to XAI systems that can handle multimodal and longitudinal data, which we postulate are important features in many clinical workflows. In this study, we review, from a clinical perspective, the current state of XAI for multimodal and longitudinal datasets and highlight the challenges thereof. Additionally, we propose the XAI orchestrator, an instance that aims to help clinicians with the synopsis of multimodal and longitudinal data, the resulting AI predictions, and the corresponding explainability output. We propose several desirable properties of the XAI orchestrator, such as being adaptive, hierarchical, interactive, and uncertainty-aware.
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Affiliation(s)
| | - Haozhe Luo
- ARTORG Center for Biomedical Engineering Research, University of Bern, Bern, Switzerland
| | - Shelley Zixin Shu
- ARTORG Center for Biomedical Engineering Research, University of Bern, Bern, Switzerland
| | - Amith Kamath
- ARTORG Center for Biomedical Engineering Research, University of Bern, Bern, Switzerland
| | - Yannick Suter
- ARTORG Center for Biomedical Engineering Research, University of Bern, Bern, Switzerland
- Department of Radiation Oncology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Mohamed Shelan
- Department of Radiation Oncology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Alexander Pöllinger
- Department of Diagnostic, Interventional and Pediatric Radiology, Inselspital, Bern University Hospital, Bern, Switzerland
| | - Mauricio Reyes
- ARTORG Center for Biomedical Engineering Research, University of Bern, Bern, Switzerland
- Department of Radiation Oncology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
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3
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Ghosh S, Bouchard C. Considerations on efforts needed to improve our understanding of the genetics of obesity. Int J Obes (Lond) 2024:10.1038/s41366-024-01528-0. [PMID: 38849463 DOI: 10.1038/s41366-024-01528-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/21/2023] [Revised: 04/18/2024] [Accepted: 04/24/2024] [Indexed: 06/09/2024]
Affiliation(s)
- Sujoy Ghosh
- Pennington Biomedical Research Center, Baton Rouge, LA, 70808, USA.
| | - Claude Bouchard
- Pennington Biomedical Research Center, Baton Rouge, LA, 70808, USA.
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Sousa RT, Silva S, Pesquita C. Explaining protein-protein interactions with knowledge graph-based semantic similarity. Comput Biol Med 2024; 170:108076. [PMID: 38308873 DOI: 10.1016/j.compbiomed.2024.108076] [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: 09/25/2023] [Revised: 12/11/2023] [Accepted: 01/27/2024] [Indexed: 02/05/2024]
Abstract
The application of artificial intelligence and machine learning methods for several biomedical applications, such as protein-protein interaction prediction, has gained significant traction in recent decades. However, explainability is a key aspect of using machine learning as a tool for scientific discovery. Explainable artificial intelligence approaches help clarify algorithmic mechanisms and identify potential bias in the data. Given the complexity of the biomedical domain, explanations should be grounded in domain knowledge which can be achieved by using ontologies and knowledge graphs. These knowledge graphs express knowledge about a domain by capturing different perspectives of the representation of real-world entities. However, the most popular way to explore knowledge graphs with machine learning is through using embeddings, which are not explainable. As an alternative, knowledge graph-based semantic similarity offers the advantage of being explainable. Additionally, similarity can be computed to capture different semantic aspects within the knowledge graph and increasing the explainability of predictive approaches. We propose a novel method to generate explainable vector representations, KGsim2vec, that uses aspect-oriented semantic similarity features to represent pairs of entities in a knowledge graph. Our approach employs a set of machine learning models, including decision trees, genetic programming, random forest and eXtreme gradient boosting, to predict relations between entities. The experiments reveal that considering multiple semantic aspects when representing the similarity between two entities improves explainability and predictive performance. KGsim2vec performs better than black-box methods based on knowledge graph embeddings or graph neural networks. Moreover, KGsim2vec produces global models that can capture biological phenomena and elucidate data biases.
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Affiliation(s)
- Rita T Sousa
- LASIGE, Faculdade de Ciências da Universidade de Lisboa, Lisboa, Portugal.
| | - Sara Silva
- LASIGE, Faculdade de Ciências da Universidade de Lisboa, Lisboa, Portugal
| | - Catia Pesquita
- LASIGE, Faculdade de Ciências da Universidade de Lisboa, Lisboa, Portugal
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5
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Sharma K, Saini N, Hasija Y. Identifying the mitochondrial metabolism network by integration of machine learning and explainable artificial intelligence in skeletal muscle in type 2 diabetes. Mitochondrion 2024; 74:101821. [PMID: 38040172 DOI: 10.1016/j.mito.2023.11.004] [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: 06/26/2023] [Revised: 10/04/2023] [Accepted: 11/26/2023] [Indexed: 12/03/2023]
Abstract
Imbalance in glucose metabolism and insulin resistance are two primary features of type 2 diabetes/diabetes mellitus. Its etiology is linked to mitochondrial dysfunction in skeletal muscle tissue. The mitochondria are vital organelles involved in ATP synthesis and metabolism. The underlying biological pathways leading to mitochondrial dysfunction in type 2 diabetes can help us understand the pathophysiology of the disease. In this study, the mitochondrial gene expression dataset were retrieved from the GSE22309, GSE25462, and GSE18732 using Mitocarta 3.0, focusing specifically on genes that are associated with mitochondrial function in type 2 disease. Feature selection on the expression dataset of skeletal muscle tissue from 107 control patients and 70 type 2 diabetes patients using the XGBoost algorithm having the highest accuracy. For interpretation and analysis of results linked to the disease by examining the feature importance deduced from the model was done using SHAP (SHapley Additive exPlanations). Next, to comprehend the biological connections, study of protein-protien and mRNA-miRNA networks was conducted using String and Mienturnet respectively. The analysis revealed BDH1, YARS2, AKAP10, RARS2, MRPS31, were potential mitochondrial target genes among the other twenty genes. These genes are mainly involved in the transport and organization of mitochondria, regulation of its membrane potential, and intrinsic apoptotic signaling etc. mRNA-miRNA interaction network revealed a significant role of miR-375; miR-30a-5p; miR-16-5p; miR-129-5p; miR-1229-3p; and miR-1224-3p; in the regulation of mitochondrial function exhibited strong associations with type 2 diabetes. These results might aid in the creation of novel targets for therapy and type 2 diabetes biomarkers.
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Affiliation(s)
- Kritika Sharma
- CSIR-Institute of Genomics and Integrative Biology, Mall Road, New Delhi 110007, India; Department of Biotechnology, Delhi Technological University, Delhi 110042, India
| | - Neeru Saini
- CSIR-Institute of Genomics and Integrative Biology, Mall Road, New Delhi 110007, India; Academy of Scientific & Innovative Research (AcSIR), Ghaziabad 201002, India
| | - Yasha Hasija
- Department of Biotechnology, Delhi Technological University, Delhi 110042, India.
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6
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Toussaint PA, Leiser F, Thiebes S, Schlesner M, Brors B, Sunyaev A. Explainable artificial intelligence for omics data: a systematic mapping study. Brief Bioinform 2023; 25:bbad453. [PMID: 38113073 PMCID: PMC10729786 DOI: 10.1093/bib/bbad453] [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: 12/23/2022] [Revised: 07/28/2023] [Accepted: 11/08/2023] [Indexed: 12/21/2023] Open
Abstract
Researchers increasingly turn to explainable artificial intelligence (XAI) to analyze omics data and gain insights into the underlying biological processes. Yet, given the interdisciplinary nature of the field, many findings have only been shared in their respective research community. An overview of XAI for omics data is needed to highlight promising approaches and help detect common issues. Toward this end, we conducted a systematic mapping study. To identify relevant literature, we queried Scopus, PubMed, Web of Science, BioRxiv, MedRxiv and arXiv. Based on keywording, we developed a coding scheme with 10 facets regarding the studies' AI methods, explainability methods and omics data. Our mapping study resulted in 405 included papers published between 2010 and 2023. The inspected papers analyze DNA-based (mostly genomic), transcriptomic, proteomic or metabolomic data by means of neural networks, tree-based methods, statistical methods and further AI methods. The preferred post-hoc explainability methods are feature relevance (n = 166) and visual explanation (n = 52), while papers using interpretable approaches often resort to the use of transparent models (n = 83) or architecture modifications (n = 72). With many research gaps still apparent for XAI for omics data, we deduced eight research directions and discuss their potential for the field. We also provide exemplary research questions for each direction. Many problems with the adoption of XAI for omics data in clinical practice are yet to be resolved. This systematic mapping study outlines extant research on the topic and provides research directions for researchers and practitioners.
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Affiliation(s)
- Philipp A Toussaint
- Department of Economics and Management, Karlsruhe Institute of Technology, Karlsruhe, Germany
- HIDSS4Health – Helmholtz Information and Data Science School for Health, Karlsruhe, Heidelberg, Germany
| | - Florian Leiser
- Department of Economics and Management, Karlsruhe Institute of Technology, Karlsruhe, Germany
| | - Scott Thiebes
- Department of Economics and Management, Karlsruhe Institute of Technology, Karlsruhe, Germany
| | - Matthias Schlesner
- Biomedical Informatics, Data Mining and Data Analytics, Faculty of Applied Computer Science and Medical Faculty, University of Augsburg, Augsburg, Germany
| | - Benedikt Brors
- Division of Applied Bioinformatics, German Cancer Research Center (DKFZ), Heidelberg, Germany
- Translational Oncology, National Center for Tumor Diseases, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Ali Sunyaev
- Department of Economics and Management, Karlsruhe Institute of Technology, Karlsruhe, Germany
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Bustos-Aibar M, Aguilera CM, Alcalá-Fdez J, Ruiz-Ojeda FJ, Plaza-Díaz J, Plaza-Florido A, Tofe I, Gil-Campos M, Gacto MJ, Anguita-Ruiz A. Shared gene expression signatures between visceral adipose and skeletal muscle tissues are associated with cardiometabolic traits in children with obesity. Comput Biol Med 2023; 163:107085. [PMID: 37399741 DOI: 10.1016/j.compbiomed.2023.107085] [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: 11/25/2022] [Revised: 04/28/2023] [Accepted: 05/27/2023] [Indexed: 07/05/2023]
Abstract
Obesity in children is related to the development of cardiometabolic complications later in life, where molecular changes of visceral adipose tissue (VAT) and skeletal muscle tissue (SMT) have been proven to be fundamental. The aim of this study is to unveil the gene expression architecture of both tissues in a cohort of Spanish boys with obesity, using a clustering method known as weighted gene co-expression network analysis. For this purpose, we have followed a multi-objective analytic pipeline consisting of three main approaches; identification of gene co-expression clusters associated with childhood obesity, individually in VAT and SMT (intra-tissue, approach I); identification of gene co-expression clusters associated with obesity-metabolic alterations, individually in VAT and SMT (intra-tissue, approach II); and identification of gene co-expression clusters associated with obesity-metabolic alterations simultaneously in VAT and SMT (inter-tissue, approach III). In both tissues, we identified independent and inter-tissue gene co-expression signatures associated with obesity and cardiovascular risk, some of which exceeded multiple-test correction filters. In these signatures, we could identify some central hub genes (e.g., NDUFB8, GUCY1B1, KCNMA1, NPR2, PPP3CC) participating in relevant metabolic pathways exceeding multiple-testing correction filters. We identified the central hub genes PIK3R2, PPP3C and PTPN5 associated with MAPK signaling and insulin resistance terms. This is the first time that these genes have been associated with childhood obesity in both tissues. Therefore, they could be potential novel molecular targets for drugs and health interventions, opening new lines of research on the personalized care in this pathology. This work generates interesting hypotheses about the transcriptomics alterations underlying metabolic health alterations in obesity in the pediatric population.
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Affiliation(s)
- Mireia Bustos-Aibar
- Department of Biochemistry and Molecular Biology II, School of Pharmacy, University of Granada, 18071, Granada, Spain.
| | - Concepción M Aguilera
- Department of Biochemistry and Molecular Biology II, School of Pharmacy, University of Granada, 18071, Granada, Spain; Biomedical Research Networking Center for Physiopathology of Obesity and Nutrition, Carlos III Health Institute, 28029, Madrid, Spain.
| | - Jesús Alcalá-Fdez
- Department of Computer Science and Artificial Intelligence, Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI), University of Granada, 18071, Granada, Spain.
| | - Francisco J Ruiz-Ojeda
- Department of Biochemistry and Molecular Biology II, School of Pharmacy, University of Granada, 18071, Granada, Spain; RG Adipocytes and Metabolism, Institute for Diabetes and Obesity, Helmholtz Diabetes Center at the Helmholtz Zentrum München, Neuherberg, 85764, Munich, Germany.
| | - Julio Plaza-Díaz
- Department of Biochemistry and Molecular Biology II, School of Pharmacy, University of Granada, 18071, Granada, Spain; Children's Hospital of Eastern Ontario Research Institute, Ottawa, ON K1H 8L1, Ontario, Canada.
| | - Abel Plaza-Florido
- PROmoting FITness and Health through physical activity research group, Sport and Health University Research Institute, Department of Physical Education and Sports, University of Granada, 18071, Granada, Spain; Pediatric Exercise and Genomics Research Center, Department of Pediatrics, School of Medicine, University of California at Irvine, Irvine, 92617, CA, United States.
| | - Inés Tofe
- Biomedical Research Networking Center for Physiopathology of Obesity and Nutrition, Carlos III Health Institute, 28029, Madrid, Spain; University Clinical Hospital, Institute Maimónides of Biomedicine Investigation of Córdoba, University of Córdoba, 14004, Córdoba, Spain.
| | - Mercedes Gil-Campos
- Biomedical Research Networking Center for Physiopathology of Obesity and Nutrition, Carlos III Health Institute, 28029, Madrid, Spain; University Clinical Hospital, Institute Maimónides of Biomedicine Investigation of Córdoba, University of Córdoba, 14004, Córdoba, Spain.
| | - María J Gacto
- Department of Software Engineering, University of Granada, 18071, Granada, Spain.
| | - Augusto Anguita-Ruiz
- Department of Biochemistry and Molecular Biology II, School of Pharmacy, University of Granada, 18071, Granada, Spain; Barcelona Institute for Global Health, ISGlobal, 08003, Barcelona, Spain.
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8
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Kırboğa KK, Abbasi S, Küçüksille EU. Explainability and white box in drug discovery. Chem Biol Drug Des 2023; 102:217-233. [PMID: 37105727 DOI: 10.1111/cbdd.14262] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2022] [Revised: 03/24/2023] [Accepted: 04/12/2023] [Indexed: 04/29/2023]
Abstract
Recently, artificial intelligence (AI) techniques have been increasingly used to overcome the challenges in drug discovery. Although traditional AI techniques generally have high accuracy rates, there may be difficulties in explaining the decision process and patterns. This can create difficulties in understanding and making sense of the outputs of algorithms used in drug discovery. Therefore, using explainable AI (XAI) techniques, the causes and consequences of the decision process are better understood. This can help further improve the drug discovery process and make the right decisions. To address this issue, Explainable Artificial Intelligence (XAI) emerged as a process and method that securely captures the results and outputs of machine learning (ML) and deep learning (DL) algorithms. Using techniques such as SHAP (SHApley Additive ExPlanations) and LIME (Locally Interpretable Model-Independent Explanations) has made the drug targeting phase clearer and more understandable. XAI methods are expected to reduce time and cost in future computational drug discovery studies. This review provides a comprehensive overview of XAI-based drug discovery and development prediction. XAI mechanisms to increase confidence in AI and modeling methods. The limitations and future directions of XAI in drug discovery are also discussed.
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Affiliation(s)
- Kevser Kübra Kırboğa
- Bioengineering Department, Bilecik Seyh Edebali University, Bilecik, Turkey
- Informatics Institute, Istanbul Technical University, Maslak, Turkey
| | - Sumra Abbasi
- Department of Biological Sciences, National of Medical Sciences, Rawalpindi, Pakistan
| | - Ecir Uğur Küçüksille
- Department of Computer Engineering, Süleyman Demirel University, Isparta, Turkey
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9
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Zhao Y, Shao J, Asmann YW. Assessment and Optimization of Explainable Machine Learning Models Applied to Transcriptomic Data. GENOMICS, PROTEOMICS & BIOINFORMATICS 2022; 20:899-911. [PMID: 35931322 PMCID: PMC10025763 DOI: 10.1016/j.gpb.2022.07.003] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/10/2021] [Revised: 06/05/2022] [Accepted: 07/25/2022] [Indexed: 01/12/2023]
Abstract
Explainable artificial intelligence aims to interpret how machine learning models make decisions, and many model explainers have been developed in the computer vision field. However, understanding of the applicability of these model explainers to biological data is still lacking. In this study, we comprehensively evaluated multiple explainers by interpreting pre-trained models for predicting tissue types from transcriptomic data and by identifying the top contributing genes from each sample with the greatest impacts on model prediction. To improve the reproducibility and interpretability of results generated by model explainers, we proposed a series of optimization strategies for each explainer on two different model architectures of multilayer perceptron (MLP) and convolutional neural network (CNN). We observed three groups of explainer and model architecture combinations with high reproducibility. Group II, which contains three model explainers on aggregated MLP models, identified top contributing genes in different tissues that exhibited tissue-specific manifestation and were potential cancer biomarkers. In summary, our work provides novel insights and guidance for exploring biological mechanisms using explainable machine learning models.
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Affiliation(s)
- Yongbing Zhao
- Department of Quantitative Health Sciences, Mayo Clinic, Jacksonville, FL 32224, USA.
| | - Jinfeng Shao
- The Laboratory of Malaria and Vector Research, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Rockville, MD 20852, USA
| | - Yan W Asmann
- Department of Quantitative Health Sciences, Mayo Clinic, Jacksonville, FL 32224, USA.
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Huynh TMT, Ni CF, Su YS, Nguyen VCN, Lee IH, Lin CP, Nguyen HH. Predicting Heavy Metal Concentrations in Shallow Aquifer Systems Based on Low-Cost Physiochemical Parameters Using Machine Learning Techniques. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:ijerph191912180. [PMID: 36231480 PMCID: PMC9566676 DOI: 10.3390/ijerph191912180] [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: 08/06/2022] [Revised: 09/20/2022] [Accepted: 09/20/2022] [Indexed: 05/07/2023]
Abstract
Monitoring ex-situ water parameters, namely heavy metals, needs time and laboratory work for water sampling and analytical processes, which can retard the response to ongoing pollution events. Previous studies have successfully applied fast modeling techniques such as artificial intelligence algorithms to predict heavy metals. However, neither low-cost feature predictability nor explainability assessments have been considered in the modeling process. This study proposes a reliable and explainable framework to find an effective model and feature set to predict heavy metals in groundwater. The integrated assessment framework has four steps: model selection uncertainty, feature selection uncertainty, predictive uncertainty, and model interpretability. The results show that Random Forest is the most suitable model, and quick-measure parameters can be used as predictors for arsenic (As), iron (Fe), and manganese (Mn). Although the model performance is auspicious, it likely produces significant uncertainties. The findings also demonstrate that arsenic is related to nutrients and spatial distribution, while Fe and Mn are affected by spatial distribution and salinity. Some limitations and suggestions are also discussed to improve the prediction accuracy and interpretability.
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Affiliation(s)
- Thi-Minh-Trang Huynh
- Graduate Institute of Applied Geology, National Central University, Taoyuan 32001, Taiwan
| | - Chuen-Fa Ni
- Graduate Institute of Applied Geology, National Central University, Taoyuan 32001, Taiwan
- Center for Environmental Studies, National Central University, Taoyuan 32001, Taiwan
- Correspondence: (C.-F.N.); (Y.-S.S.)
| | - Yu-Sheng Su
- Department of Computer Science and Engineering, National Taiwan Ocean University, Keelung 202301, Taiwan
- Correspondence: (C.-F.N.); (Y.-S.S.)
| | - Vo-Chau-Ngan Nguyen
- College of Environment and Natural Resources, Can Tho University, Can Tho 94000, Vietnam
| | - I-Hsien Lee
- Graduate Institute of Applied Geology, National Central University, Taoyuan 32001, Taiwan
- Center for Environmental Studies, National Central University, Taoyuan 32001, Taiwan
| | - Chi-Ping Lin
- Graduate Institute of Applied Geology, National Central University, Taoyuan 32001, Taiwan
- Center for Environmental Studies, National Central University, Taoyuan 32001, Taiwan
| | - Hoang-Hiep Nguyen
- Graduate Institute of Applied Geology, National Central University, Taoyuan 32001, Taiwan
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11
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Abstract
High-throughput technologies such as next-generation sequencing allow biologists to observe cell function with unprecedented resolution, but the resulting datasets are too large and complicated for humans to understand without the aid of advanced statistical methods. Machine learning (ML) algorithms, which are designed to automatically find patterns in data, are well suited to this task. Yet these models are often so complex as to be opaque, leaving researchers with few clues about underlying mechanisms. Interpretable machine learning (iML) is a burgeoning subdiscipline of computational statistics devoted to making the predictions of ML models more intelligible to end users. This article is a gentle and critical introduction to iML, with an emphasis on genomic applications. I define relevant concepts, motivate leading methodologies, and provide a simple typology of existing approaches. I survey recent examples of iML in genomics, demonstrating how such techniques are increasingly integrated into research workflows. I argue that iML solutions are required to realize the promise of precision medicine. However, several open challenges remain. I examine the limitations of current state-of-the-art tools and propose a number of directions for future research. While the horizon for iML in genomics is wide and bright, continued progress requires close collaboration across disciplines.
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Affiliation(s)
- David S Watson
- Department of Statistical Science, University College London, London, UK.
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12
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Ray A. Machine learning in postgenomic biology and personalized medicine. WILEY INTERDISCIPLINARY REVIEWS. DATA MINING AND KNOWLEDGE DISCOVERY 2022; 12:e1451. [PMID: 35966173 PMCID: PMC9371441 DOI: 10.1002/widm.1451] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/23/2020] [Accepted: 12/22/2021] [Indexed: 06/15/2023]
Abstract
In recent years Artificial Intelligence in the form of machine learning has been revolutionizing biology, biomedical sciences, and gene-based agricultural technology capabilities. Massive data generated in biological sciences by rapid and deep gene sequencing and protein or other molecular structure determination, on the one hand, requires data analysis capabilities using machine learning that are distinctly different from classical statistical methods; on the other, these large datasets are enabling the adoption of novel data-intensive machine learning algorithms for the solution of biological problems that until recently had relied on mechanistic model-based approaches that are computationally expensive. This review provides a bird's eye view of the applications of machine learning in post-genomic biology. Attempt is also made to indicate as far as possible the areas of research that are poised to make further impacts in these areas, including the importance of explainable artificial intelligence (XAI) in human health. Further contributions of machine learning are expected to transform medicine, public health, agricultural technology, as well as to provide invaluable gene-based guidance for the management of complex environments in this age of global warming.
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Affiliation(s)
- Animesh Ray
- Riggs School of Applied Life Sciences, Keck Graduate Institute, 535 Watson Drive, Claremont, CA91711, USA
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, California, USA
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13
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Coba L, Confalonieri R, Zanker M. RecoXplainer: A Library for Development and Offline Evaluation of Explainable Recommender Systems. IEEE COMPUT INTELL M 2022. [DOI: 10.1109/mci.2021.3129958] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
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14
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A Systematic Review of Explainable Artificial Intelligence in Terms of Different Application Domains and Tasks. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12031353] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
Artificial intelligence (AI) and machine learning (ML) have recently been radically improved and are now being employed in almost every application domain to develop automated or semi-automated systems. To facilitate greater human acceptability of these systems, explainable artificial intelligence (XAI) has experienced significant growth over the last couple of years with the development of highly accurate models but with a paucity of explainability and interpretability. The literature shows evidence from numerous studies on the philosophy and methodologies of XAI. Nonetheless, there is an evident scarcity of secondary studies in connection with the application domains and tasks, let alone review studies following prescribed guidelines, that can enable researchers’ understanding of the current trends in XAI, which could lead to future research for domain- and application-specific method development. Therefore, this paper presents a systematic literature review (SLR) on the recent developments of XAI methods and evaluation metrics concerning different application domains and tasks. This study considers 137 articles published in recent years and identified through the prominent bibliographic databases. This systematic synthesis of research articles resulted in several analytical findings: XAI methods are mostly developed for safety-critical domains worldwide, deep learning and ensemble models are being exploited more than other types of AI/ML models, visual explanations are more acceptable to end-users and robust evaluation metrics are being developed to assess the quality of explanations. Research studies have been performed on the addition of explanations to widely used AI/ML models for expert users. However, more attention is required to generate explanations for general users from sensitive domains such as finance and the judicial system.
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15
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A Survey on Artificial Intelligence (AI) and eXplainable AI in Air Traffic Management: Current Trends and Development with Future Research Trajectory. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12031295] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Air Traffic Management (ATM) will be more complex in the coming decades due to the growth and increased complexity of aviation and has to be improved in order to maintain aviation safety. It is agreed that without significant improvement in this domain, the safety objectives defined by international organisations cannot be achieved and a risk of more incidents/accidents is envisaged. Nowadays, computer science plays a major role in data management and decisions made in ATM. Nonetheless, despite this, Artificial Intelligence (AI), which is one of the most researched topics in computer science, has not quite reached end users in ATM domain. In this paper, we analyse the state of the art with regards to usefulness of AI within aviation/ATM domain. It includes research work of the last decade of AI in ATM, the extraction of relevant trends and features, and the extraction of representative dimensions. We analysed how the general and ATM eXplainable Artificial Intelligence (XAI) works, analysing where and why XAI is needed, how it is currently provided, and the limitations, then synthesise the findings into a conceptual framework, named the DPP (Descriptive, Predictive, Prescriptive) model, and provide an example of its application in a scenario in 2030. It concludes that AI systems within ATM need further research for their acceptance by end-users. The development of appropriate XAI methods including the validation by appropriate authorities and end-users are key issues that needs to be addressed.
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16
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Wesołowski S, Lemmon G, Hernandez EJ, Henrie A, Miller TA, Weyhrauch D, Puchalski MD, Bray BE, Shah RU, Deshmukh VG, Delaney R, Yost HJ, Eilbeck K, Tristani-Firouzi M, Yandell M. An explainable artificial intelligence approach for predicting cardiovascular outcomes using electronic health records. PLOS DIGITAL HEALTH 2022; 1:e0000004. [PMID: 35373216 PMCID: PMC8975108 DOI: 10.1371/journal.pdig.0000004] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/31/2021] [Accepted: 11/17/2021] [Indexed: 11/19/2022]
Abstract
Understanding the conditionally-dependent clinical variables that drive cardiovascular health outcomes is a major challenge for precision medicine. Here, we deploy a recently developed massively scalable comorbidity discovery method called Poisson Binomial based Comorbidity discovery (PBC), to analyze Electronic Health Records (EHRs) from the University of Utah and Primary Children's Hospital (over 1.6 million patients and 77 million visits) for comorbid diagnoses, procedures, and medications. Using explainable Artificial Intelligence (AI) methodologies, we then tease apart the intertwined, conditionally-dependent impacts of comorbid conditions and demography upon cardiovascular health, focusing on the key areas of heart transplant, sinoatrial node dysfunction and various forms of congenital heart disease. The resulting multimorbidity networks make possible wide-ranging explorations of the comorbid and demographic landscapes surrounding these cardiovascular outcomes, and can be distributed as web-based tools for further community-based outcomes research. The ability to transform enormous collections of EHRs into compact, portable tools devoid of Protected Health Information solves many of the legal, technological, and data-scientific challenges associated with large-scale EHR analyses.
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Affiliation(s)
- Sergiusz Wesołowski
- Department of Human Genetics and Utah Center for Genetic Discovery, University of Utah, Salt Lake City, UT, United States of America
| | - Gordon Lemmon
- Department of Human Genetics and Utah Center for Genetic Discovery, University of Utah, Salt Lake City, UT, United States of America
| | - Edgar J. Hernandez
- Department of Human Genetics and Utah Center for Genetic Discovery, University of Utah, Salt Lake City, UT, United States of America
| | - Alex Henrie
- Department of Human Genetics and Utah Center for Genetic Discovery, University of Utah, Salt Lake City, UT, United States of America
| | - Thomas A. Miller
- Division of Pediatric Cardiology, University of Utah School of Medicine, Salt Lake City, UT, United States of America
| | - Derek Weyhrauch
- Division of Pediatric Cardiology, University of Utah School of Medicine, Salt Lake City, UT, United States of America
| | - Michael D. Puchalski
- Division of Pediatric Cardiology, University of Utah School of Medicine, Salt Lake City, UT, United States of America
| | - Bruce E. Bray
- Division of Cardiovascular Medicine, University of Utah School of Medicine, Salt Lake City, UT, United States of America
- University of Utah, Biomedical Informatics, Salt Lake City, UT 84108, United States of America
| | - Rashmee U. Shah
- Division of Cardiovascular Medicine, University of Utah School of Medicine, Salt Lake City, UT, United States of America
| | - Vikrant G. Deshmukh
- University of Utah Health Care CMIO Office, Salt Lake City, UT, United States of America
| | - Rebecca Delaney
- Department of Population Health Sciences, University of Utah, Salt Lake City, UT, United States of America
| | - H. Joseph Yost
- Molecular Medicine Program, University of Utah, Salt Lake City, UT, United States of America
| | - Karen Eilbeck
- Department of Population Health Sciences, University of Utah, Salt Lake City, UT, United States of America
| | - Martin Tristani-Firouzi
- Division of Pediatric Cardiology, University of Utah School of Medicine, Salt Lake City, UT, United States of America
- Nora Eccles Harrison CVRTI, University of Utah School of Medicine, Salt Lake City, UT, United States of America
| | - Mark Yandell
- Department of Human Genetics and Utah Center for Genetic Discovery, University of Utah, Salt Lake City, UT, United States of America
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17
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AIM in Medical Informatics. Artif Intell Med 2022. [DOI: 10.1007/978-3-030-64573-1_32] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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18
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Kim J, Kim HJ, Kim C, Lee JH, Kim KW, Park YM, Kim HW, Ki SY, Kim YM, Kim WH. Weakly-supervised deep learning for ultrasound diagnosis of breast cancer. Sci Rep 2021; 11:24382. [PMID: 34934144 PMCID: PMC8692405 DOI: 10.1038/s41598-021-03806-7] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2021] [Accepted: 11/30/2021] [Indexed: 11/21/2022] Open
Abstract
Conventional deep learning (DL) algorithm requires full supervision of annotating the region of interest (ROI) that is laborious and often biased. We aimed to develop a weakly-supervised DL algorithm that diagnosis breast cancer at ultrasound without image annotation. Weakly-supervised DL algorithms were implemented with three networks (VGG16, ResNet34, and GoogLeNet) and trained using 1000 unannotated US images (500 benign and 500 malignant masses). Two sets of 200 images (100 benign and 100 malignant masses) were used for internal and external validation sets. For comparison with fully-supervised algorithms, ROI annotation was performed manually and automatically. Diagnostic performances were calculated as the area under the receiver operating characteristic curve (AUC). Using the class activation map, we determined how accurately the weakly-supervised DL algorithms localized the breast masses. For internal validation sets, the weakly-supervised DL algorithms achieved excellent diagnostic performances, with AUC values of 0.92–0.96, which were not statistically different (all Ps > 0.05) from those of fully-supervised DL algorithms with either manual or automated ROI annotation (AUC, 0.92–0.96). For external validation sets, the weakly-supervised DL algorithms achieved AUC values of 0.86–0.90, which were not statistically different (Ps > 0.05) or higher (P = 0.04, VGG16 with automated ROI annotation) from those of fully-supervised DL algorithms (AUC, 0.84–0.92). In internal and external validation sets, weakly-supervised algorithms could localize 100% of malignant masses, except for ResNet34 (98%). The weakly-supervised DL algorithms developed in the present study were feasible for US diagnosis of breast cancer with well-performing localization and differential diagnosis.
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Affiliation(s)
- Jaeil Kim
- School of Computer Science and Engineering, Kyungpook National University, Daegu, Republic of Korea
| | - Hye Jung Kim
- Department of Radiology, School of Medicine, Kyungpook National University, Kyungpook National University Chilgok Hospital, Daegu, Republic of Korea
| | - Chanho Kim
- School of Computer Science and Engineering, Kyungpook National University, Daegu, Republic of Korea
| | - Jin Hwa Lee
- Department of Radiology, Dong-A University College of Medicine, Busan, Republic of Korea
| | - Keum Won Kim
- Departments of Radiology, School of Medicine, Konyang University, Konyang Univeristy Hospital, Daejeon, Republic of Korea
| | - Young Mi Park
- Department of Radiology, School of Medicine, Inje University, Busan Paik Hospital, Busan, Republic of Korea
| | - Hye Won Kim
- Department of Radiology, Wonkwang University Hospital, Wonkwang University School of Medicine, Iksan, Republic of Korea
| | - So Yeon Ki
- Department of Radiology, School of Medicine, Chonnam National University, Chonnam National University Hwasun Hospital, Hwasun, Republic of Korea
| | - You Me Kim
- Department of Radiology, School of Medicine, Dankook University, Dankook University Hospital, Cheonan, Republic of Korea
| | - Won Hwa Kim
- Department of Radiology, School of Medicine, Kyungpook National University, Kyungpook National University Chilgok Hospital, Daegu, Republic of Korea.
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19
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Cai Y, Zhang X, Kovalsky SZ, Ghashghaei HT, Greenbaum A. Detection and classification of neurons and glial cells in the MADM mouse brain using RetinaNet. PLoS One 2021; 16:e0257426. [PMID: 34559842 PMCID: PMC8462685 DOI: 10.1371/journal.pone.0257426] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2021] [Accepted: 08/31/2021] [Indexed: 12/16/2022] Open
Abstract
The ability to automatically detect and classify populations of cells in tissue sections is paramount in a wide variety of applications ranging from developmental biology to pathology. Although deep learning algorithms are widely applied to microscopy data, they typically focus on segmentation which requires extensive training and labor-intensive annotation. Here, we utilized object detection networks (neural networks) to detect and classify targets in complex microscopy images, while simplifying data annotation. To this end, we used a RetinaNet model to classify genetically labeled neurons and glia in the brains of Mosaic Analysis with Double Markers (MADM) mice. Our initial RetinaNet-based model achieved an average precision of 0.90 across six classes of cells differentiated by MADM reporter expression and their phenotype (neuron or glia). However, we found that a single RetinaNet model often failed when encountering dense and saturated glial clusters, which show high variability in their shape and fluorophore densities compared to neurons. To overcome this, we introduced a second RetinaNet model dedicated to the detection of glia clusters. Merging the predictions of the two computational models significantly improved the automated cell counting of glial clusters. The proposed cell detection workflow will be instrumental in quantitative analysis of the spatial organization of cellular populations, which is applicable not only to preparations in neuroscience studies, but also to any tissue preparation containing labeled populations of cells.
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Affiliation(s)
- Yuheng Cai
- Joint Department of Biomedical Engineering, North Carolina State University and University of North Carolina at Chapel Hill, Raleigh, North Carolina, United States of America
- Comparative Medicine Institute, North Carolina State University, Raleigh, North Carolina, United States of America
| | - Xuying Zhang
- Comparative Medicine Institute, North Carolina State University, Raleigh, North Carolina, United States of America
- Department of Molecular Biomedical Sciences, North Carolina State University, Raleigh, North Carolina, United States of America
| | - Shahar Z. Kovalsky
- Department of Mathematics, University of North Carolina, Chapel Hill, North Carolina, United States of America
| | - H. Troy Ghashghaei
- Comparative Medicine Institute, North Carolina State University, Raleigh, North Carolina, United States of America
- Department of Molecular Biomedical Sciences, North Carolina State University, Raleigh, North Carolina, United States of America
| | - Alon Greenbaum
- Joint Department of Biomedical Engineering, North Carolina State University and University of North Carolina at Chapel Hill, Raleigh, North Carolina, United States of America
- Comparative Medicine Institute, North Carolina State University, Raleigh, North Carolina, United States of America
- Bioinformatics Research Center, North Carolina State University, Raleigh, North Carolina, United States of America
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20
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Lee EE, Torous J, De Choudhury M, Depp CA, Graham SA, Kim HC, Paulus MP, Krystal JH, Jeste DV. Artificial Intelligence for Mental Health Care: Clinical Applications, Barriers, Facilitators, and Artificial Wisdom. BIOLOGICAL PSYCHIATRY. COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2021; 6:856-864. [PMID: 33571718 PMCID: PMC8349367 DOI: 10.1016/j.bpsc.2021.02.001] [Citation(s) in RCA: 47] [Impact Index Per Article: 15.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/16/2020] [Revised: 02/01/2021] [Accepted: 02/02/2021] [Indexed: 12/19/2022]
Abstract
Artificial intelligence (AI) is increasingly employed in health care fields such as oncology, radiology, and dermatology. However, the use of AI in mental health care and neurobiological research has been modest. Given the high morbidity and mortality in people with psychiatric disorders, coupled with a worsening shortage of mental health care providers, there is an urgent need for AI to help identify high-risk individuals and provide interventions to prevent and treat mental illnesses. While published research on AI in neuropsychiatry is rather limited, there is a growing number of successful examples of AI's use with electronic health records, brain imaging, sensor-based monitoring systems, and social media platforms to predict, classify, or subgroup mental illnesses as well as problems such as suicidality. This article is the product of a study group held at the American College of Neuropsychopharmacology conference in 2019. It provides an overview of AI approaches in mental health care, seeking to help with clinical diagnosis, prognosis, and treatment, as well as clinical and technological challenges, focusing on multiple illustrative publications. Although AI could help redefine mental illnesses more objectively, identify them at a prodromal stage, personalize treatments, and empower patients in their own care, it must address issues of bias, privacy, transparency, and other ethical concerns. These aspirations reflect human wisdom, which is more strongly associated than intelligence with individual and societal well-being. Thus, the future AI or artificial wisdom could provide technology that enables more compassionate and ethically sound care to diverse groups of people.
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Affiliation(s)
- Ellen E Lee
- Department of Psychiatry, University of California San Diego, San Diego, California; Sam and Rose Stein Institute for Research on Aging, University of California San Diego, San Diego, California; VA San Diego Healthcare System, San Diego, California
| | - John Torous
- Department of Psychiatry, Beth Israel Deaconess Medical Center and Harvard University, Boston, Massachusetts
| | - Munmun De Choudhury
- School of Interactive Computing, Georgia Institute of Technology, Atlanta, Georgia
| | - Colin A Depp
- Department of Psychiatry, University of California San Diego, San Diego, California; Sam and Rose Stein Institute for Research on Aging, University of California San Diego, San Diego, California; VA San Diego Healthcare System, San Diego, California
| | - Sarah A Graham
- Department of Psychiatry, University of California San Diego, San Diego, California; Sam and Rose Stein Institute for Research on Aging, University of California San Diego, San Diego, California
| | - Ho-Cheol Kim
- AI and Cognitive Software, IBM Research-Almaden, San Jose, California
| | | | - John H Krystal
- Department of Psychiatry, Yale University, New Haven, Connecticut
| | - Dilip V Jeste
- Department of Psychiatry, University of California San Diego, San Diego, California; Department of Neurosciences, University of California San Diego, San Diego, California; Sam and Rose Stein Institute for Research on Aging, University of California San Diego, San Diego, California.
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21
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Shaban-Nejad A, Michalowski M, Brownstein J, Buckeridge D. Guest Editorial Explainable AI: Towards Fairness, Accountability, Transparency and Trust in Healthcare. IEEE J Biomed Health Inform 2021. [DOI: 10.1109/jbhi.2021.3088832] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
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22
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Park Y, Heider D, Hauschild AC. Integrative Analysis of Next-Generation Sequencing for Next-Generation Cancer Research toward Artificial Intelligence. Cancers (Basel) 2021; 13:3148. [PMID: 34202427 PMCID: PMC8269018 DOI: 10.3390/cancers13133148] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2021] [Revised: 06/16/2021] [Accepted: 06/21/2021] [Indexed: 12/18/2022] Open
Abstract
The rapid improvement of next-generation sequencing (NGS) technologies and their application in large-scale cohorts in cancer research led to common challenges of big data. It opened a new research area incorporating systems biology and machine learning. As large-scale NGS data accumulated, sophisticated data analysis methods became indispensable. In addition, NGS data have been integrated with systems biology to build better predictive models to determine the characteristics of tumors and tumor subtypes. Therefore, various machine learning algorithms were introduced to identify underlying biological mechanisms. In this work, we review novel technologies developed for NGS data analysis, and we describe how these computational methodologies integrate systems biology and omics data. Subsequently, we discuss how deep neural networks outperform other approaches, the potential of graph neural networks (GNN) in systems biology, and the limitations in NGS biomedical research. To reflect on the various challenges and corresponding computational solutions, we will discuss the following three topics: (i) molecular characteristics, (ii) tumor heterogeneity, and (iii) drug discovery. We conclude that machine learning and network-based approaches can add valuable insights and build highly accurate models. However, a well-informed choice of learning algorithm and biological network information is crucial for the success of each specific research question.
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Affiliation(s)
- Youngjun Park
- Department of Mathematics and Computer Science, Philipps-University of Marburg, 35032 Marburg, Germany; (Y.P.); (D.H.)
| | - Dominik Heider
- Department of Mathematics and Computer Science, Philipps-University of Marburg, 35032 Marburg, Germany; (Y.P.); (D.H.)
| | - Anne-Christin Hauschild
- Department of Mathematics and Computer Science, Philipps-University of Marburg, 35032 Marburg, Germany; (Y.P.); (D.H.)
- Department of Medical Informatics, University Medical Center Göttingen, 37075 Göttingen, Germany
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23
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Bruno P, Calimeri F, Greco G. AIM in Medical Informatics. Artif Intell Med 2021. [DOI: 10.1007/978-3-030-58080-3_32-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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