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Robles-Hernández JSL, Medina DI, Aguirre-Hurtado K, Bosquez M, Salcedo R, Miralrio A. AI-assisted models to predict chemotherapy drugs modified with C 60 fullerene derivatives. BEILSTEIN JOURNAL OF NANOTECHNOLOGY 2024; 15:1170-1188. [PMID: 39319207 PMCID: PMC11420546 DOI: 10.3762/bjnano.15.95] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/28/2024] [Accepted: 08/30/2024] [Indexed: 09/26/2024]
Abstract
Employing quantitative structure-activity relationship (QSAR)/ quantitative structure-property relationship (QSPR) models, this study explores the application of fullerene derivatives as nanocarriers for breast cancer chemotherapy drugs. Isolated drugs and two drug-fullerene complexes (i.e., drug-pristine C60 fullerene and drug-carboxyfullerene C60-COOH) were investigated with the protein CXCR7 as the molecular docking target. The research involved over 30 drugs and employed Pearson's hard-soft acid-base theory and common QSAR/QSPR descriptors to build predictive models for the docking scores. Energetic descriptors were computed using quantum chemistry at the density functional-based tight binding DFTB3 level. The results indicate that drug-fullerene complexes interact more with CXCR7 than isolated drugs. Specific binding sites were identified, with varying locations for each drug complex. Predictive models, developed using multiple linear regression and IBM Watson artificial intelligence (AI), achieved mean absolute percentage errors below 12%, driven by AI-identified key variables. The predictive models included mainly quantitative descriptors collected from datasets as well as computed ones. In addition, a water-soluble fullerene was used to compare results obtained by DFTB3 with a conventional density functional theory approach. These findings promise to enhance breast cancer chemotherapy by leveraging fullerene-based drug nanocarriers.
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Affiliation(s)
| | - Dora Iliana Medina
- Tecnologico de Monterrey, Institute of Advanced Materials for Sustainable Manufacturing, Monterrey 64849, Mexico
| | - Katerin Aguirre-Hurtado
- Instituto de Investigaciones en Materiales, Universidad Nacional Autónoma de México, Circuito exterior s/n, Ciudad Universitaria, Coyoacán, 04510, Ciudad de México, Mexico
| | - Marlene Bosquez
- Instituto de Investigaciones en Materiales, Universidad Nacional Autónoma de México, Circuito exterior s/n, Ciudad Universitaria, Coyoacán, 04510, Ciudad de México, Mexico
| | - Roberto Salcedo
- Instituto de Investigaciones en Materiales, Universidad Nacional Autónoma de México, Circuito exterior s/n, Ciudad Universitaria, Coyoacán, 04510, Ciudad de México, Mexico
| | - Alan Miralrio
- Tecnologico de Monterrey, Escuela de Ingeniería y Ciencias, Ave. Eugenio Garza Sada 2501, Monterrey 64849, Mexico
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2
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Anstey C, Ullman D, Su L, Su C, Siniard C, Simmons S, Edberg J, Williams LA. The practical use of artificial intelligence in Transfusion Medicine and Apheresis. Transfus Apher Sci 2024; 63:104001. [PMID: 39265225 DOI: 10.1016/j.transci.2024.104001] [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: 05/17/2024] [Revised: 09/05/2024] [Accepted: 09/06/2024] [Indexed: 09/14/2024]
Abstract
BACKGROUND Blood and plasma volume calculations are a daily part of practice for many Transfusion Medicine and Apheresis practitioners. Though many formulas exist, each facility may have their own modifications to consider. ChatGPT (Generative Pre-trained Transformer) provides a new and exciting pathway for those with no programming experience to create personalized programs to meet the demands of daily practice. Additionally, this pathway creates computer programs that provide accurate and reproducible outputs. Herein, we aimed to create a step-by-step process for clinicians to create customized computer programs for use in everyday practice. METHODS We created a process of inputs to ChatGPT-40, which generated computer programming code. This code was copied and pasted into Notepad (and saved as a Python file) and Google Colaboratory to verify functionality. We validated the durability of our process by repeating it over a 5-day timeframe and by recruiting volunteers to reproduce our outputs using the suggested process. RESULTS Computer code generated by ChatGPT-40 in response to our common language inputs was accurate and durable over time. The code was fully functional in both Python and Colaboratory. Volunteers reproduced our process and outputs with minimal assistance. CONCLUSION We analyzed the practical application of ChatGPT-40 and artificial intelligence (AI) to perform daily calculations encountered in Transfusion Medicine. Our results provide a proof of concept that people with no programming experience can create customizable solutions for their own facilities. Our future work will expand to the creation of comprehensive and customizable websites designed for each individual user.
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Affiliation(s)
- Celine Anstey
- Department of Biology - University of South Carolina, Columbia, SC, USA
| | - David Ullman
- Professional Pathology Services, Columbia, SC, USA
| | - Leon Su
- Department of Laboratory Medicine and Pathology - Mayo Clinic Hospital, Phoenix, AZ, USA
| | - Chuying Su
- Alix School of Medicine - Mayo Clinic Arizona, Scottsdale, AZ, USA
| | - Chad Siniard
- Department of Pathology - University of Alabama at Birmingham, Birmingham, AL, USA
| | - Sierra Simmons
- Independent researcher - Transfusion Medicine, Las Vegas, NV, USA
| | - Jesse Edberg
- Independent researcher - Lead Senior Macintosh Engineer, Phoenix, AZ, USA
| | - Lance A Williams
- Department of Laboratory Medicine and Pathology - Mayo Clinic Hospital, Phoenix, AZ, USA.
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3
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Singh S, Kaur N, Gehlot A. Application of artificial intelligence in drug design: A review. Comput Biol Med 2024; 179:108810. [PMID: 38991316 DOI: 10.1016/j.compbiomed.2024.108810] [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: 03/18/2024] [Revised: 05/31/2024] [Accepted: 06/24/2024] [Indexed: 07/13/2024]
Abstract
Artificial intelligence (AI) is a field of computer science that involves acquiring information, developing rule bases, and mimicking human behaviour. The fundamental concept behind AI is to create intelligent computer systems that can operate with minimal human intervention or without any intervention at all. These rule-based systems are developed using various machine learning and deep learning models, enabling them to solve complex problems. AI is integrated with these models to learn, understand, and analyse provided data. The rapid advancement of Artificial Intelligence (AI) is reshaping numerous industries, with the pharmaceutical sector experiencing a notable transformation. AI is increasingly being employed to automate, optimize, and personalize various facets of the pharmaceutical industry, particularly in pharmacological research. Traditional drug development methods areknown for being time-consuming, expensive, and less efficient, often taking around a decade and costing billions of dollars. The integration of artificial intelligence (AI) techniques addresses these challenges by enabling the examination of compounds with desired properties from a vast pool of input drugs. Furthermore, it plays a crucial role in drug screening by predicting toxicity, bioactivity, ADME properties (absorption, distribution, metabolism, and excretion), physicochemical properties, and more. AI enhances the drug design process by improving the efficiency and accuracy of predicting drug behaviour, interactions, and properties. These approaches further significantly improve the precision of drug discovery processes and decrease clinical trial costs leading to the development of more effective drugs.
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Affiliation(s)
- Simrandeep Singh
- Department of Electronics & Communication Engineering, UCRD, Chandigarh University, Gharuan, Punjab, India.
| | - Navjot Kaur
- Department of Pharmacognosy, Amar Shaheed Baba Ajit Singh Jujhar Singh Memorial College of Pharmacy, Bela, Ropar, India
| | - Anita Gehlot
- Uttaranchal Institute of technology, Uttaranchal University, Dehradun, India
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4
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Yilmaz S, Tasyurek M, Amuk M, Celik M, Canger EM. Developing deep learning methods for classification of teeth in dental panoramic radiography. Oral Surg Oral Med Oral Pathol Oral Radiol 2024; 138:118-127. [PMID: 37316425 DOI: 10.1016/j.oooo.2023.02.021] [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: 02/23/2022] [Revised: 09/13/2022] [Accepted: 02/10/2023] [Indexed: 06/16/2023]
Abstract
OBJECTIVES We aimed to develop an artificial intelligence-based clinical dental decision-support system using deep-learning methods to reduce diagnostic interpretation error and time and increase the effectiveness of dental treatment and classification. STUDY DESIGN We compared the performance of 2 deep-learning methods, You Only Look Once V4 (YOLO-V4) and Faster Regions with the Convolutional Neural Networks (R-CNN), for tooth classification in dental panoramic radiography for tooth classification in dental panoramic radiography to determine which is more successful in terms of accuracy, time, and detection ability. Using a method based on deep-learning models trained on a semantic segmentation task, we analyzed 1200 panoramic radiographs selected retrospectively. In the classification process, our model identified 36 classes, including 32 teeth and 4 impacted teeth. RESULTS The YOLO-V4 method achieved a mean 99.90% precision, 99.18% recall, and 99.54% F1 score. The Faster R-CNN method achieved a mean 93.67% precision, 90.79% recall, and 92.21% F1 score. Experimental evaluations showed that the YOLO-V4 method outperformed the Faster R-CNN method in terms of accuracy of predicted teeth in the tooth classification process, speed of tooth classification, and ability to detect impacted and erupted third molars. CONCLUSIONS The YOLO-V4 method outperforms the Faster R-CNN method in terms of accuracy of tooth prediction, speed of detection, and ability to detect impacted third molars and erupted third molars. The proposed deep learning based methods can assist dentists in clinical decision making, save time, and reduce the negative effects of stress and fatigue in daily practice.
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Affiliation(s)
- Serkan Yilmaz
- Faculty of Dentistry, Department of Oral and Maxillofacial Radiology, Erciyes University, Kayseri, Turkey
| | - Murat Tasyurek
- Department of Computer Engineering, Kayseri University, Kayseri, Turkey
| | - Mehmet Amuk
- Faculty of Dentistry, Department of Oral and Maxillofacial Radiology, Erciyes University, Kayseri, Turkey
| | - Mete Celik
- Department of Computer Engineering, Erciyes University, Kayseri, Turkey
| | - Emin Murat Canger
- Faculty of Dentistry, Department of Oral and Maxillofacial Radiology, Erciyes University, Kayseri, Turkey.
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Tyagin I, Safro I. Dyport: dynamic importance-based biomedical hypothesis generation benchmarking technique. BMC Bioinformatics 2024; 25:213. [PMID: 38872097 PMCID: PMC11177514 DOI: 10.1186/s12859-024-05812-8] [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: 01/31/2024] [Accepted: 05/16/2024] [Indexed: 06/15/2024] Open
Abstract
BACKGROUND Automated hypothesis generation (HG) focuses on uncovering hidden connections within the extensive information that is publicly available. This domain has become increasingly popular, thanks to modern machine learning algorithms. However, the automated evaluation of HG systems is still an open problem, especially on a larger scale. RESULTS This paper presents a novel benchmarking framework Dyport for evaluating biomedical hypothesis generation systems. Utilizing curated datasets, our approach tests these systems under realistic conditions, enhancing the relevance of our evaluations. We integrate knowledge from the curated databases into a dynamic graph, accompanied by a method to quantify discovery importance. This not only assesses hypotheses accuracy but also their potential impact in biomedical research which significantly extends traditional link prediction benchmarks. Applicability of our benchmarking process is demonstrated on several link prediction systems applied on biomedical semantic knowledge graphs. Being flexible, our benchmarking system is designed for broad application in hypothesis generation quality verification, aiming to expand the scope of scientific discovery within the biomedical research community. CONCLUSIONS Dyport is an open-source benchmarking framework designed for biomedical hypothesis generation systems evaluation, which takes into account knowledge dynamics, semantics and impact. All code and datasets are available at: https://github.com/IlyaTyagin/Dyport .
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Affiliation(s)
- Ilya Tyagin
- Center for Bioinformatics and Computational Biology, University of Delaware, Newark, DE, 19713, USA.
| | - Ilya Safro
- Department of Computer and Information Sciences, University of Delaware, Newark, DE, 19716, USA.
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Vu TD, Luong DT, Ho TT, Nguyen Thi TM, Singh V, Chu DT. Drug repurposing for regenerative medicine and cosmetics: Scientific, technological and economic issues. PROGRESS IN MOLECULAR BIOLOGY AND TRANSLATIONAL SCIENCE 2024; 207:337-353. [PMID: 38942543 DOI: 10.1016/bs.pmbts.2024.02.005] [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: 06/30/2024]
Abstract
Regenerative medicine and cosmetics are currently two outstanding fields for drug discovery. Although many pharmaceutical products for regenerative medicine and cosmetics have received approval by official agencies, several challenges are still needed to overcome, especially financial and time issues. As a result, drug repositioning, which is the usage of previously approved drugs for new treatment, stands out as a promising approach to tackle these problems. Recently, increasing scientific evidence is collected to demonstrate the applicability of this novel method in the field of regenerative medicine and cosmetics. Experts in drug development have also taken advantage of novel technologies to discover new candidates for repositioning purposes following computational approach, one of two main approaches of drug repositioning. Therefore, numerous repurposed candidates have obtained approval to enter the market and have witnessed financial success such as minoxidil and fingolimod. The benefits of drug repositioning are undeniable for regenerative medicine and cosmetics. However, some aspects still need to be carefully considered regarding this method including actual effectiveness during clinical trials, patent regulations, data integration and analysis, publicly unavailable databases as well as environmental concerns and more effort are required to overcome these obstacles.
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Affiliation(s)
- Thuy-Duong Vu
- Center for Biomedicine and Community Health, International School, Vietnam National University, Hanoi, Vietnam
| | - Duc Tri Luong
- Center for Biomedicine and Community Health, International School, Vietnam National University, Hanoi, Vietnam
| | - Thuy-Tien Ho
- Center for Biomedicine and Community Health, International School, Vietnam National University, Hanoi, Vietnam
| | - Thuy-My Nguyen Thi
- Center for Biomedicine and Community Health, International School, Vietnam National University, Hanoi, Vietnam
| | - Vijai Singh
- Department of Biosciences, School of Science, Indrashil University, Rajpur, Mehsana, India
| | - Dinh-Toi Chu
- Center for Biomedicine and Community Health, International School, Vietnam National University, Hanoi, Vietnam; Faculty of Applied Sciences, International School, Vietnam National University, Hanoi, Vietnam.
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Mishra A, Vasanthan M, Malliappan SP. Drug Repurposing: A Leading Strategy for New Threats and Targets. ACS Pharmacol Transl Sci 2024; 7:915-932. [PMID: 38633585 PMCID: PMC11019736 DOI: 10.1021/acsptsci.3c00361] [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: 12/13/2023] [Revised: 03/01/2024] [Accepted: 03/06/2024] [Indexed: 04/19/2024]
Abstract
Less than 6% of rare illnesses have an appropriate treatment option. Repurposed medications for new indications are a cost-effective and time-saving strategy that results in excellent success rates, which may significantly lower the risk associated with therapeutic development for rare illnesses. It is becoming a realistic alternative to repurposing "conventional" medications to treat joint and rare diseases considering the significant failure rates, high expenses, and sluggish stride of innovative medication advancement. This is due to delisted compounds, cheaper research fees, and faster development time frames. Repurposed drug competitors have been developed using strategic decisions based on data analysis, interpretation, and investigational approaches, but technical and regulatory restrictions must also be considered. Combining experimental and computational methodologies generates innovative new medicinal applications. It is a one-of-a-kind strategy for repurposing human-safe pharmaceuticals to treat uncommon and difficult-to-treat ailments. It is a very effective method for discovering and creating novel medications. Several pharmaceutical firms have developed novel therapies by repositioning old medications. Repurposing drugs is practical, cost-effective, and speedy and generally involves lower risks when compared to developing a new drug from the beginning.
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Affiliation(s)
- Ashish
Sriram Mishra
- Department
of Pharmaceutics, SRM College of Pharmacy, SRM Institute of Science and Technology, Kattankulathur, 603202, Tamil Nadu, India
| | - Manimaran Vasanthan
- Department
of Pharmaceutics, SRM College of Pharmacy, SRM Institute of Science and Technology, Kattankulathur, 603202, Tamil Nadu, India
| | - Sivakumar Ponnurengam Malliappan
- School
of Medicine and Pharmacy, Duy Tan University, Da Nang Vietnam, Institute
of Research and Development, Duy Tan University, Da Nang 550000, Vietnam
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8
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Li Y, Wu X, Fang D, Luo Y. Informing immunotherapy with multi-omics driven machine learning. NPJ Digit Med 2024; 7:67. [PMID: 38486092 PMCID: PMC10940614 DOI: 10.1038/s41746-024-01043-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2023] [Accepted: 02/14/2024] [Indexed: 03/18/2024] Open
Abstract
Progress in sequencing technologies and clinical experiments has revolutionized immunotherapy on solid and hematologic malignancies. However, the benefits of immunotherapy are limited to specific patient subsets, posing challenges for broader application. To improve its effectiveness, identifying biomarkers that can predict patient response is crucial. Machine learning (ML) play a pivotal role in harnessing multi-omic cancer datasets and unlocking new insights into immunotherapy. This review provides an overview of cutting-edge ML models applied in omics data for immunotherapy analysis, including immunotherapy response prediction and immunotherapy-relevant tumor microenvironment identification. We elucidate how ML leverages diverse data types to identify significant biomarkers, enhance our understanding of immunotherapy mechanisms, and optimize decision-making process. Additionally, we discuss current limitations and challenges of ML in this rapidly evolving field. Finally, we outline future directions aimed at overcoming these barriers and improving the efficiency of ML in immunotherapy research.
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Affiliation(s)
- Yawei Li
- Department of Preventive Medicine, Northwestern University, Feinberg School of Medicine, Chicago, IL, 60611, USA
- Center for Collaborative AI in Healthcare, Northwestern University, Feinberg School of Medicine, Chicago, IL, 60611, USA
| | - Xin Wu
- Department of Medicine, University of Illinois at Chicago, Chicago, IL, 60612, USA
| | - Deyu Fang
- Department of Pathology, Northwestern University Feinberg School of Medicine, Chicago, IL, 60611, USA
| | - Yuan Luo
- Department of Preventive Medicine, Northwestern University, Feinberg School of Medicine, Chicago, IL, 60611, USA.
- Center for Collaborative AI in Healthcare, Northwestern University, Feinberg School of Medicine, Chicago, IL, 60611, USA.
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9
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Özateş ME, Yaman A, Salami F, Campos S, Wolf SI, Schneider U. Identification and interpretation of gait analysis features and foot conditions by explainable AI. Sci Rep 2024; 14:5998. [PMID: 38472287 PMCID: PMC10933258 DOI: 10.1038/s41598-024-56656-4] [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: 05/19/2023] [Accepted: 03/08/2024] [Indexed: 03/14/2024] Open
Abstract
Clinical gait analysis is a crucial step for identifying foot disorders and planning surgery. Automating this process is essential for efficiently assessing the substantial amount of gait data. In this study, we explored the potential of state-of-the-art machine learning (ML) and explainable artificial intelligence (XAI) algorithms to automate all various steps involved in gait analysis for six specific foot conditions. To address the complexity of gait data, we manually created new features, followed by recursive feature elimination using Support Vector Machines (SVM) and Random Forests (RF) to eliminate low-variance features. SVM, RF, K-nearest Neighbor (KNN), and Logistic Regression (LREGR) were compared for classification, with a Majority Voting (MV) model combining trained models. KNN and MV achieved mean balanced accuracy, recall, precision, and F1 score of 0.87. All models were interpreted using Local Interpretable Model-agnostic Explanation (LIME) method and the five most relevant features were identified for each foot condition. High success scores indicate a strong relationship between selected features and foot conditions, potentially indicating clinical relevance. The proposed ML pipeline, adaptable for other foot conditions, showcases its potential in aiding experts in foot condition identification and planning surgeries.
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Affiliation(s)
| | - Alper Yaman
- Fraunhofer IPA, Nobelstrasse 12, Stuttgart, Germany.
| | - Firooz Salami
- Clinic for Orthopedics, Heidelberg University Hospital, Schlierbacher Landstrasse 200a, 69118, Heidelberg, Germany
| | - Sarah Campos
- Clinic for Orthopedics, Heidelberg University Hospital, Schlierbacher Landstrasse 200a, 69118, Heidelberg, Germany
| | - Sebastian I Wolf
- Clinic for Orthopedics, Heidelberg University Hospital, Schlierbacher Landstrasse 200a, 69118, Heidelberg, Germany
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Hoier D, Groß-Ophoff-Müller C, Franklin C, Hallek M, von Stebut E, Elter T, Mauch C, Kreuzberg N, Koll P. Digital decision support for structural improvement of melanoma tumor boards: using standard cases to optimize workflow. J Cancer Res Clin Oncol 2024; 150:115. [PMID: 38457085 PMCID: PMC10923955 DOI: 10.1007/s00432-024-05627-3] [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: 05/31/2023] [Accepted: 01/18/2024] [Indexed: 03/09/2024]
Abstract
PURPOSE Choosing optimal cancer treatment is challenging, and certified cancer centers must present all patients in multidisciplinary tumor boards (MDT). Our aim was to develop a decision support system (DSS) to provide treatment recommendations for apparently simple cases already at conference registration and to classify these as "standard cases". According to certification requirements, discussion of standard cases is optional and would thus allow more time for complex cases. METHODS We created a smartphone query that simulated a tumor conference registration and requested all information needed to provide a recommendation. In total, 111 out of 705 malignant melanoma cases discussed at a skin cancer center from 2017 to 2020 were identified as potential standard cases, for which a digital twin recommendation was then generated by DSS. RESULTS The system provided reliable advice in all 111 cases and showed 97% concordance of MDT and DSS for therapeutic recommendations, regardless of tumor stage. Discrepancies included two cases (2%) where DSS advised discussions at MDT and one case (1%) with deviating recommendation due to advanced patient age. CONCLUSIONS Our work aimed not to replace clinical expertise but to alleviate MDT workload and enhance focus on complex cases. Overall, our DSS proved to be a suitable tool for identifying standard cases as such, providing correct treatment recommendations, and thus reducing the time burden of tumor conferences in favor for the comprehensive discussion of complex cases. The aim is to implement the DSS in routine tumor board software for further qualitative assessment of its impact on oncological care.
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Affiliation(s)
- David Hoier
- Department I of Internal Medicine, Center for Integrated Oncology Aachen Bonn Cologne Duesseldorf, University of Cologne, Faculty of Medicine and University Hospital Cologne, Cologne, Germany.
| | | | - Cindy Franklin
- Department of Dermatology and Venereology, Center for Integrated Oncology Aachen Bonn Cologne Duesseldorf, University of Cologne, Faculty of Medicine and University Hospital Cologne, Cologne, Germany
| | - Michael Hallek
- Department I of Internal Medicine, Center for Integrated Oncology Aachen Bonn Cologne Duesseldorf, University of Cologne, Faculty of Medicine and University Hospital Cologne, Cologne, Germany
| | - Esther von Stebut
- Department of Dermatology and Venereology, Center for Integrated Oncology Aachen Bonn Cologne Duesseldorf, University of Cologne, Faculty of Medicine and University Hospital Cologne, Cologne, Germany
| | - Thomas Elter
- Department I of Internal Medicine, Center for Integrated Oncology Aachen Bonn Cologne Duesseldorf, University of Cologne, Faculty of Medicine and University Hospital Cologne, Cologne, Germany
| | - Cornelia Mauch
- Department of Dermatology and Venereology, Center for Integrated Oncology Aachen Bonn Cologne Duesseldorf, University of Cologne, Faculty of Medicine and University Hospital Cologne, Cologne, Germany
- Department of Dermatology, Venereology and Allergology, Ruhr-University Bochum, Bochum, Germany
| | - Nicole Kreuzberg
- Department of Dermatology and Venereology, Center for Integrated Oncology Aachen Bonn Cologne Duesseldorf, University of Cologne, Faculty of Medicine and University Hospital Cologne, Cologne, Germany
| | - Philipp Koll
- Department of Dermatology and Venereology, Center for Integrated Oncology Aachen Bonn Cologne Duesseldorf, University of Cologne, Faculty of Medicine and University Hospital Cologne, Cologne, Germany
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11
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Niazi SK, Mariam Z. Computer-Aided Drug Design and Drug Discovery: A Prospective Analysis. Pharmaceuticals (Basel) 2023; 17:22. [PMID: 38256856 PMCID: PMC10819513 DOI: 10.3390/ph17010022] [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: 11/10/2023] [Revised: 12/13/2023] [Accepted: 12/20/2023] [Indexed: 01/24/2024] Open
Abstract
In the dynamic landscape of drug discovery, Computer-Aided Drug Design (CADD) emerges as a transformative force, bridging the realms of biology and technology. This paper overviews CADDs historical evolution, categorization into structure-based and ligand-based approaches, and its crucial role in rationalizing and expediting drug discovery. As CADD advances, incorporating diverse biological data and ensuring data privacy become paramount. Challenges persist, demanding the optimization of algorithms and robust ethical frameworks. Integrating Machine Learning and Artificial Intelligence amplifies CADDs predictive capabilities, yet ethical considerations and scalability challenges linger. Collaborative efforts and global initiatives, exemplified by platforms like Open-Source Malaria, underscore the democratization of drug discovery. The convergence of CADD with personalized medicine offers tailored therapeutic solutions, though ethical dilemmas and accessibility concerns must be navigated. Emerging technologies like quantum computing, immersive technologies, and green chemistry promise to redefine the future of CADD. The trajectory of CADD, marked by rapid advancements, anticipates challenges in ensuring accuracy, addressing biases in AI, and incorporating sustainability metrics. This paper concludes by highlighting the need for proactive measures in navigating the ethical, technological, and educational frontiers of CADD to shape a healthier, brighter future in drug discovery.
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Affiliation(s)
| | - Zamara Mariam
- Centre for Health and Life Sciences, Coventry University, Coventry City CV1 5FB, UK
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12
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Zhang N, Zhang Z. The application of cognitive neuroscience to judicial models: recent progress and trends. Front Neurosci 2023; 17:1257004. [PMID: 37811324 PMCID: PMC10556240 DOI: 10.3389/fnins.2023.1257004] [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: 07/11/2023] [Accepted: 08/31/2023] [Indexed: 10/10/2023] Open
Abstract
Legal prediction presents one of the most significant challenges when applying artificial intelligence (AI) to the legal field. The legal system is a complex adaptive system characterized by the ambiguity of legal language and the diversity of value functions. The imprecision and procedural knowledge inherent in law makes judicial issues difficult to be expressed in a computer symbol system. Current semantic processing and machine learning technologies cannot fully capture the complex nature of legal relations, thereby raising doubts about the accuracy of legal predictions and reliability of judicial models. Cognitive computing, designed to emulate human brain functions and aid in enhancing decision-making processes, offers a better understanding of legal data and the processes of legal reasoning. This paper discusses the advancements made in cognitive methods applied to legal concept learning, semantic extraction, judicial data processing, legal reasoning, understanding of judicial bias, and the interpretability of judicial models. The integration of cognitive neuroscience with law has facilitated several constructive attempts, indicating that the evolution of cognitive law could be the next frontier in the intersection of AI and legal practice.
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Affiliation(s)
- Ni Zhang
- Department of Sichuan University, Chengdu, China
| | - Zixuan Zhang
- School of Law, Sichuan University, Chengdu, China
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13
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Simon Davis DA, Ritchie M, Hammill D, Garrett J, Slater RO, Otoo N, Orlov A, Gosling K, Price J, Yip D, Jung K, Syed FM, Atmosukarto II, Quah BJC. Identifying cancer-associated leukocyte profiles using high-resolution flow cytometry screening and machine learning. Front Immunol 2023; 14:1211064. [PMID: 37600768 PMCID: PMC10435879 DOI: 10.3389/fimmu.2023.1211064] [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: 04/24/2023] [Accepted: 06/26/2023] [Indexed: 08/22/2023] Open
Abstract
Background Machine learning (ML) is a valuable tool with the potential to aid clinical decision making. Adoption of ML to this end requires data that reliably correlates with the clinical outcome of interest; the advantage of ML is that it can model these correlations from complex multiparameter data sets that can be difficult to interpret conventionally. While currently available clinical data can be used in ML for this purpose, there exists the potential to discover new "biomarkers" that will enhance the effectiveness of ML in clinical decision making. Since the interaction of the immune system and cancer is a hallmark of tumor establishment and progression, one potential area for cancer biomarker discovery is through the investigation of cancer-related immune cell signatures. Hence, we hypothesize that blood immune cell signatures can act as a biomarker for cancer progression. Methods To probe this, we have developed and tested a multiparameter cell-surface marker screening pipeline, using flow cytometry to obtain high-resolution systemic leukocyte population profiles that correlate with detection and characterization of several cancers in murine syngeneic tumor models. Results We discovered a signature of several blood leukocyte subsets, the most notable of which were monocyte subsets, that could be used to train CATboost ML models to predict the presence and type of cancer present in the animals. Conclusions Our findings highlight the potential utility of a screening approach to identify robust leukocyte biomarkers for cancer detection and characterization. This pipeline can easily be adapted to screen for cancer specific leukocyte markers from the blood of cancer patient.
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Affiliation(s)
- David A. Simon Davis
- Irradiation Immunity Interaction Lab, Australian National University, Canberra, ACT, Australia
| | - Melissa Ritchie
- Irradiation Immunity Interaction Lab, Australian National University, Canberra, ACT, Australia
| | - Dillon Hammill
- Division of Genome Sciences & Cancer, John Curtin School of Medical Research, Australian National University, Canberra, ACT, Australia
| | - Jessica Garrett
- Division of Genome Sciences & Cancer, John Curtin School of Medical Research, Australian National University, Canberra, ACT, Australia
| | - Robert O. Slater
- Division of Genome Sciences & Cancer, John Curtin School of Medical Research, Australian National University, Canberra, ACT, Australia
| | - Naomi Otoo
- Division of Genome Sciences & Cancer, John Curtin School of Medical Research, Australian National University, Canberra, ACT, Australia
| | - Anna Orlov
- Division of Genome Sciences & Cancer, John Curtin School of Medical Research, Australian National University, Canberra, ACT, Australia
| | - Katharine Gosling
- Irradiation Immunity Interaction Lab, Australian National University, Canberra, ACT, Australia
| | - Jason Price
- Division of Genome Sciences & Cancer, John Curtin School of Medical Research, Australian National University, Canberra, ACT, Australia
| | - Desmond Yip
- Australian National University, Canberra, ACT, Australia
- Department of Medical Oncology, Canberra Hospital & Health Services, Canberra, ACT, Australia
| | - Kylie Jung
- Irradiation Immunity Interaction Lab, Australian National University, Canberra, ACT, Australia
- Radiation Oncology Department, Canberra Hospital & Health Services, Canberra, ACT, Australia
| | - Farhan M. Syed
- Irradiation Immunity Interaction Lab, Australian National University, Canberra, ACT, Australia
- Radiation Oncology Department, Canberra Hospital & Health Services, Canberra, ACT, Australia
| | - Ines I. Atmosukarto
- Irradiation Immunity Interaction Lab, Australian National University, Canberra, ACT, Australia
- Division of Genome Sciences & Cancer, John Curtin School of Medical Research, Australian National University, Canberra, ACT, Australia
| | - Ben J. C. Quah
- Irradiation Immunity Interaction Lab, Australian National University, Canberra, ACT, Australia
- Radiation Oncology Department, Canberra Hospital & Health Services, Canberra, ACT, Australia
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Chakraborty C, Bhattacharya M, Dhama K, Agoramoorthy G. Artificial intelligence-enabled clinical trials might be a faster way to perform rapid clinical trials and counter future pandemics: lessons learned from the COVID-19 period. Int J Surg 2023; 109:1535-1538. [PMID: 36906740 PMCID: PMC10389411 DOI: 10.1097/js9.0000000000000088] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2022] [Accepted: 11/20/2022] [Indexed: 03/13/2023]
Affiliation(s)
- Chiranjib Chakraborty
- Department of Biotechnology, School of Life Science and Biotechnology, Adamas University, Kolkata, West Bengal
| | | | - Kuldeep Dhama
- Division of Pathology, ICAR-Indian Veterinary Research Institute, Bareilly, Uttar Pradesh, India
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15
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Gombolay GY, Gopalan N, Bernasconi A, Nabbout R, Megerian JT, Siegel B, Hallman-Cooper J, Bhalla S, Gombolay MC. Review of Machine Learning and Artificial Intelligence (ML/AI) for the Pediatric Neurologist. Pediatr Neurol 2023; 141:42-51. [PMID: 36773406 PMCID: PMC10040433 DOI: 10.1016/j.pediatrneurol.2023.01.004] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/22/2022] [Revised: 01/03/2023] [Accepted: 01/09/2023] [Indexed: 01/15/2023]
Abstract
Artificial intelligence (AI) and a popular branch of AI known as machine learning (ML) are increasingly being utilized in medicine and to inform medical research. This review provides an overview of AI and ML (AI/ML), including definitions of common terms. We discuss the history of AI and provide instances of how AI/ML can be applied to pediatric neurology. Examples include imaging in neuro-oncology, autism diagnosis, diagnosis from charts, epilepsy, cerebral palsy, and neonatal neurology. Topics such as supervised learning, unsupervised learning, and reinforcement learning are discussed.
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Affiliation(s)
- Grace Y Gombolay
- Division of Neurology, Department of Pediatrics, Emory University School of Medicine, Atlanta Georgia; Division of Pediatric Neurology, Children's Healthcare of Atlanta, Atlanta Georgia.
| | - Nakul Gopalan
- Georgia Institute of Technology, Interactive Computing, Atlanta, Georgia
| | - Andrea Bernasconi
- Neuroimaging of Epilepsy Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, UK
| | - Rima Nabbout
- Department of Pediatric Neurology, Necker Enfants Malades Hospital, Reference Centre for Rare Epilepsies and Member of the ERN EpiCARE, Imagine Institute UMR1163, Paris Descartes University, Paris, France
| | - Jonathan T Megerian
- Department of Pediatrics, CHOC Children's, Irvine School of Medicine, University of California, Orange, California
| | - Benjamin Siegel
- Division of Neurology, Department of Pediatrics, Emory University School of Medicine, Atlanta Georgia; Division of Pediatric Neurology, Children's Healthcare of Atlanta, Atlanta Georgia
| | - Jamika Hallman-Cooper
- Division of Neurology, Department of Pediatrics, Emory University School of Medicine, Atlanta Georgia; Division of Pediatric Neurology, Children's Healthcare of Atlanta, Atlanta Georgia
| | - Sonam Bhalla
- Division of Neurology, Department of Pediatrics, Emory University School of Medicine, Atlanta Georgia; Division of Pediatric Neurology, Children's Healthcare of Atlanta, Atlanta Georgia
| | - Matthew C Gombolay
- Georgia Institute of Technology, Interactive Computing, Atlanta, Georgia
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16
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Visibelli A, Roncaglia B, Spiga O, Santucci A. The Impact of Artificial Intelligence in the Odyssey of Rare Diseases. Biomedicines 2023; 11:887. [PMID: 36979866 PMCID: PMC10045927 DOI: 10.3390/biomedicines11030887] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2023] [Revised: 02/28/2023] [Accepted: 03/08/2023] [Indexed: 03/16/2023] Open
Abstract
Emerging machine learning (ML) technologies have the potential to significantly improve the research and treatment of rare diseases, which constitute a vast set of diseases that affect a small proportion of the total population. Artificial Intelligence (AI) algorithms can help to quickly identify patterns and associations that would be difficult or impossible for human analysts to detect. Predictive modeling techniques, such as deep learning, have been used to forecast the progression of rare diseases, enabling the development of more targeted treatments. Moreover, AI has also shown promise in the field of drug development for rare diseases with the identification of subpopulations of patients who may be most likely to respond to a particular drug. This review aims to highlight the achievements of AI algorithms in the study of rare diseases in the past decade and advise researchers on which methods have proven to be most effective. The review will focus on specific rare diseases, as defined by a prevalence rate that does not exceed 1-9/100,000 on Orphanet, and will examine which AI methods have been most successful in their study. We believe this review can guide clinicians and researchers in the successful application of ML in rare diseases.
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Affiliation(s)
- Anna Visibelli
- Department of Biotechnology, Chemistry and Pharmacy, University of Siena, 53100 Siena, Italy
| | - Bianca Roncaglia
- Department of Biotechnology, Chemistry and Pharmacy, University of Siena, 53100 Siena, Italy
| | - Ottavia Spiga
- Department of Biotechnology, Chemistry and Pharmacy, University of Siena, 53100 Siena, Italy
- Competence Center ARTES 4.0, 53100 Siena, Italy
- SienabioACTIVE—SbA, 53100 Siena, Italy
| | - Annalisa Santucci
- Department of Biotechnology, Chemistry and Pharmacy, University of Siena, 53100 Siena, Italy
- Competence Center ARTES 4.0, 53100 Siena, Italy
- SienabioACTIVE—SbA, 53100 Siena, Italy
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17
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Goeckenjan M, Festag A, Kather JN. Entscheidungsfindungen in der Reproduktionsmedizin. GYNAKOLOGISCHE ENDOKRINOLOGIE 2023. [DOI: 10.1007/s10304-022-00489-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023]
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18
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Laufs D, Peters M, Schultz C. Data platforms for open life sciences-A systematic analysis of management instruments. PLoS One 2022; 17:e0276204. [PMID: 36282849 PMCID: PMC9595524 DOI: 10.1371/journal.pone.0276204] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2022] [Accepted: 10/02/2022] [Indexed: 11/05/2022] Open
Abstract
Open data platforms are interfaces between data demand of and supply from their users. Yet, data platform providers frequently struggle to aggregate data to suit their users' needs and to establish a high intensity of data exchange in a collaborative environment. Here, using open life science data platforms as an example for a diverse data structure, we systematically categorize these platforms based on their technology intermediation and the range of domains they cover to derive general and specific success factors for their management instruments. Our qualitative content analysis is based on 39 in-depth interviews with experts employed by data platforms and external stakeholders. We thus complement peer initiatives which focus solely on data quality, by additionally highlighting the data platforms' role to enable data utilization for innovative output. Based on our analysis, we propose a clearly structured and detailed guideline for seven management instruments. This guideline helps to establish and operationalize data platforms and to best exploit the data provided. Our findings support further exploitation of the open innovation potential in the life sciences and beyond.
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Affiliation(s)
- Daniel Laufs
- Technology Management Research Group, Faculty of Business, Economics and Social Sciences, Kiel University, Kiel, SH, Germany
| | - Mareike Peters
- Technology Management Research Group, Faculty of Business, Economics and Social Sciences, Kiel University, Kiel, SH, Germany
| | - Carsten Schultz
- Technology Management Research Group, Faculty of Business, Economics and Social Sciences, Kiel University, Kiel, SH, Germany
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19
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Jing X, Indani A, Hubig N, Min H, Gong Y, Cimino JJ, Sittig DF, Rennert L, Robinson D, Biondich P, Wright A, Nøhr C, Law T, Faxvaag A, Gimbel R. A Systematic Approach to Configuring MetaMap for Optimal Performance. Methods Inf Med 2022; 61:e51-e63. [PMID: 35613942 PMCID: PMC9788913 DOI: 10.1055/a-1862-0421] [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] [Indexed: 02/08/2023]
Abstract
BACKGROUND MetaMap is a valuable tool for processing biomedical texts to identify concepts. Although MetaMap is highly configurative, configuration decisions are not straightforward. OBJECTIVE To develop a systematic, data-driven methodology for configuring MetaMap for optimal performance. METHODS MetaMap, the word2vec model, and the phrase model were used to build a pipeline. For unsupervised training, the phrase and word2vec models used abstracts related to clinical decision support as input. During testing, MetaMap was configured with the default option, one behavior option, and two behavior options. For each configuration, cosine and soft cosine similarity scores between identified entities and gold-standard terms were computed for 40 annotated abstracts (422 sentences). The similarity scores were used to calculate and compare the overall percentages of exact matches, similar matches, and missing gold-standard terms among the abstracts for each configuration. The results were manually spot-checked. The precision, recall, and F-measure (β =1) were calculated. RESULTS The percentages of exact matches and missing gold-standard terms were 0.6-0.79 and 0.09-0.3 for one behavior option, and 0.56-0.8 and 0.09-0.3 for two behavior options, respectively. The percentages of exact matches and missing terms for soft cosine similarity scores exceeded those for cosine similarity scores. The average precision, recall, and F-measure were 0.59, 0.82, and 0.68 for exact matches, and 1.00, 0.53, and 0.69 for missing terms, respectively. CONCLUSION We demonstrated a systematic approach that provides objective and accurate evidence guiding MetaMap configurations for optimizing performance. Combining objective evidence and the current practice of using principles, experience, and intuitions outperforms a single strategy in MetaMap configurations. Our methodology, reference codes, measurements, results, and workflow are valuable references for optimizing and configuring MetaMap.
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Affiliation(s)
- Xia Jing
- Department of Public Health Sciences, College of Behavioral, Social and Health Sciences, Clemson University, Clemson, South Carolina, United States,Address for correspondence Xia Jing, MD, PhD Department of Public Health Sciences, College of Behavioral, Social and Health Sciences, Clemson UniversityEdwards Hall 511, Clemson, SC 29634United States
| | - Akash Indani
- School of Computing, College of Engineering, Computing and Applied Sciences, Clemson University, Clemson, South Carolina, United States
| | - Nina Hubig
- School of Computing, College of Engineering, Computing and Applied Sciences, Clemson University, Clemson, South Carolina, United States
| | - Hua Min
- Department of Health Administration and Policy, College of Health and Human Services, George Mason University, Fairfax, Virginia, United States
| | - Yang Gong
- School of Biomedical Informatics, The University of Texas Health Sciences Center at Houston, Houston, Texas, United States
| | - James J. Cimino
- Informatics Institute, The University of Alabama at Birmingham, Birmingham, Alabama, United States
| | - Dean F. Sittig
- School of Biomedical Informatics, The University of Texas Health Sciences Center at Houston, Houston, Texas, United States
| | - Lior Rennert
- Department of Public Health Sciences, College of Behavioral, Social and Health Sciences, Clemson University, Clemson, South Carolina, United States
| | | | - Paul Biondich
- Department of Pediatrics, Clem McDonald Biomedical Informatics Center, Regenstrief Institute, Indiana University School of Medicine, Indianapolis, Indiana, United States
| | - Adam Wright
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, United States
| | - Christian Nøhr
- Department of Planning, Faculty of Engineering, Aalborg University, Aalborg, Denmark
| | - Timothy Law
- Ohio Musculoskeletal and Neurologic Institute, Ohio University, Athens, Ohio, United States
| | - Arild Faxvaag
- Department of Neuromedicine and Movement Science, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Trondheim, Norway
| | - Ronald Gimbel
- Department of Public Health Sciences, College of Behavioral, Social and Health Sciences, Clemson University, Clemson, South Carolina, United States
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20
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Petrick LM, Shomron N. AI/ML-driven advances in untargeted metabolomics and exposomics for biomedical applications. CELL REPORTS. PHYSICAL SCIENCE 2022; 3:100978. [PMID: 35936554 PMCID: PMC9354369 DOI: 10.1016/j.xcrp.2022.100978] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Metabolomics describes a high-throughput approach for measuring a repertoire of metabolites and small molecules in biological samples. One utility of untargeted metabolomics, unbiased global analysis of the metabolome, is to detect key metabolites as contributors to, or readouts of, human health and disease. In this perspective, we discuss how artificial intelligence (AI) and machine learning (ML) have promoted major advances in untargeted metabolomics workflows and facilitated pivotal findings in the areas of disease screening and diagnosis. We contextualize applications of AI and ML to the emerging field of high-resolution mass spectrometry (HRMS) exposomics, which unbiasedly detects endogenous metabolites and exogenous chemicals in human tissue to characterize exposure linked with disease outcomes. We discuss the state of the science and suggest potential opportunities for using AI and ML to improve data quality, rigor, detection, and chemical identification in untargeted metabolomics and exposomics studies.
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Affiliation(s)
- Lauren M. Petrick
- The Bert Strassburger Metabolic Center, Sheba Medical Center, Tel-Hashomer, Israel
- Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Institute for Exposomics Research, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Noam Shomron
- Faculty of Medicine, Edmond J. Safra Center for Bioinformatics, Sagol School of Neuroscience, Center for Nanoscience and Nanotechnology, Center for Innovation Laboratories (TILabs), Tel Aviv University, Tel Aviv, Israel
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21
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Truong TTT, Panizzutti B, Kim JH, Walder K. Repurposing Drugs via Network Analysis: Opportunities for Psychiatric Disorders. Pharmaceutics 2022; 14:1464. [PMID: 35890359 PMCID: PMC9319329 DOI: 10.3390/pharmaceutics14071464] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2022] [Revised: 06/30/2022] [Accepted: 07/12/2022] [Indexed: 02/04/2023] Open
Abstract
Despite advances in pharmacology and neuroscience, the path to new medications for psychiatric disorders largely remains stagnated. Drug repurposing offers a more efficient pathway compared with de novo drug discovery with lower cost and less risk. Various computational approaches have been applied to mine the vast amount of biomedical data generated over recent decades. Among these methods, network-based drug repurposing stands out as a potent tool for the comprehension of multiple domains of knowledge considering the interactions or associations of various factors. Aligned well with the poly-pharmacology paradigm shift in drug discovery, network-based approaches offer great opportunities to discover repurposing candidates for complex psychiatric disorders. In this review, we present the potential of network-based drug repurposing in psychiatry focusing on the incentives for using network-centric repurposing, major network-based repurposing strategies and data resources, applications in psychiatry and challenges of network-based drug repurposing. This review aims to provide readers with an update on network-based drug repurposing in psychiatry. We expect the repurposing approach to become a pivotal tool in the coming years to battle debilitating psychiatric disorders.
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Affiliation(s)
- Trang T. T. Truong
- IMPACT, The Institute for Mental and Physical Health and Clinical Translation, School of Medicine, Deakin University, Geelong 3220, Australia; (T.T.T.T.); (B.P.); (J.H.K.)
| | - Bruna Panizzutti
- IMPACT, The Institute for Mental and Physical Health and Clinical Translation, School of Medicine, Deakin University, Geelong 3220, Australia; (T.T.T.T.); (B.P.); (J.H.K.)
| | - Jee Hyun Kim
- IMPACT, The Institute for Mental and Physical Health and Clinical Translation, School of Medicine, Deakin University, Geelong 3220, Australia; (T.T.T.T.); (B.P.); (J.H.K.)
- Mental Health Theme, The Florey Institute of Neuroscience and Mental Health, Parkville 3010, Australia
| | - Ken Walder
- IMPACT, The Institute for Mental and Physical Health and Clinical Translation, School of Medicine, Deakin University, Geelong 3220, Australia; (T.T.T.T.); (B.P.); (J.H.K.)
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22
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Guijas C, Horton LE, Hoang L, Domingo-Almenara X, Billings EM, Ware BC, Sullivan B, Siuzdak G. Microbial Metabolite 3-Indolepropionic Acid Mediates Immunosuppression. Metabolites 2022; 12:metabo12070645. [PMID: 35888769 PMCID: PMC9317520 DOI: 10.3390/metabo12070645] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2022] [Revised: 06/27/2022] [Accepted: 07/06/2022] [Indexed: 01/07/2023] Open
Abstract
The microbial-derived metabolite, 3-indolepropionic acid (3-IPA), has been intensely studied since its origins were discovered in 2009; however, 3-IPA's role in immunosuppression has had limited attention. Untargeted metabolomic analyses of T-cell exhaustion and immunosuppression, represented by dysfunctional under-responsive CD8+ T cells, reveal a potential role of 3-IPA in these responses. T-cell exhaustion was examined via infection of two genetically related mouse strains, DBA/1J and DBA/2J, with lymphocytic choriomeningitis virus (LCMV) Clone 13 (Cl13). The different mouse strains produced disparate outcomes driven by their T-cell responses. Infected DBA/2J presented with exhausted T cells and persistent infection, and DBA/1J mice died one week after infection from cytotoxic T lymphocytes (CTLs)-mediated pulmonary failure. Metabolomics revealed over 70 metabolites were altered between the DBA/1J and DBA/2J models over the course of the infection, most of them in mice with a fatal outcome. Cognitive-driven prioritization combined with statistical significance and fold change were used to prioritize the metabolites. 3-IPA, a tryptophan-derived metabolite, was identified as a high-priority candidate for testing. To test its activity 3-IPA was added to the drinking water of the mouse models during LCMV Cl13 infection, with the results showing that 3-IPA allowed the mice to survive longer. This negative immune-modulation effect might be of interest for the modulation of CTL responses in events such as autoimmune diseases, type I diabetes or even COVID-19. Moreover, 3-IPA's bacterial origin raises the possibility of targeting the microbiome to enhance CTL responses in diseases such as cancer and chronic infection.
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Affiliation(s)
- Carlos Guijas
- Scripps Center for Metabolomics, The Scripps Research Institute, 10550 North Torrey Pines Road, La Jolla, CA 92037, USA; (C.G.); (L.H.); (E.M.B.)
| | - Lucy E. Horton
- Department of Immunology and Microbiology, The Scripps Research Institute, 10550 North Torrey Pines Road, La Jolla, CA 92037, USA; (L.E.H.); (B.C.W.)
| | - Linh Hoang
- Scripps Center for Metabolomics, The Scripps Research Institute, 10550 North Torrey Pines Road, La Jolla, CA 92037, USA; (C.G.); (L.H.); (E.M.B.)
| | - Xavier Domingo-Almenara
- Computational Metabolomics for Systems Biology Lab, Omics Sciences Unit, Eurecat—Technology Centre of Catalonia, 08005 Barcelona, Catalonia, Spain;
| | - Elizabeth M. Billings
- Scripps Center for Metabolomics, The Scripps Research Institute, 10550 North Torrey Pines Road, La Jolla, CA 92037, USA; (C.G.); (L.H.); (E.M.B.)
| | - Brian C. Ware
- Department of Immunology and Microbiology, The Scripps Research Institute, 10550 North Torrey Pines Road, La Jolla, CA 92037, USA; (L.E.H.); (B.C.W.)
| | - Brian Sullivan
- Department of Immunology and Microbiology, The Scripps Research Institute, 10550 North Torrey Pines Road, La Jolla, CA 92037, USA; (L.E.H.); (B.C.W.)
- Correspondence: (B.S.); (G.S.); Tel.: +1-858-784-9425 (G.S.)
| | - Gary Siuzdak
- Scripps Center for Metabolomics, The Scripps Research Institute, 10550 North Torrey Pines Road, La Jolla, CA 92037, USA; (C.G.); (L.H.); (E.M.B.)
- Departments of Chemistry, Molecular, and Computational Biology, The Scripps Research Institute, 10550 North Torrey Pines Road, La Jolla, CA 92037, USA
- Correspondence: (B.S.); (G.S.); Tel.: +1-858-784-9425 (G.S.)
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23
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Beltran-Perez C, Serrano AAA, Solís-Rosas G, Martínez-Jiménez A, Orozco-Cruz R, Espinoza-Vázquez A, Miralrio A. A General Use QSAR-ARX Model to Predict the Corrosion Inhibition Efficiency of Drugs in Terms of Quantum Mechanical Descriptors and Experimental Comparison for Lidocaine. Int J Mol Sci 2022; 23:ijms23095086. [PMID: 35563474 PMCID: PMC9099790 DOI: 10.3390/ijms23095086] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2022] [Revised: 04/22/2022] [Accepted: 04/27/2022] [Indexed: 11/16/2022] Open
Abstract
A study of 250 commercial drugs to act as corrosion inhibitors on steel has been developed by applying the quantitative structure-activity relationship (QSAR) paradigm. Hard-soft acid-base (HSAB) descriptors were used to establish a mathematical model to predict the corrosion inhibition efficiency (IE%) of several commercial drugs on steel surfaces. These descriptors were calculated through third-order density-functional tight binding (DFTB) methods. The mathematical modeling was carried out through autoregressive with exogenous inputs (ARX) framework and tested by fivefold cross-validation. Another set of drugs was used as an external validation, obtaining SD, RMSE, and MSE, obtaining 6.76%, 3.89%, 7.03%, and 49.47%, respectively. With a predicted value of IE% = 87.51%, lidocaine was selected to perform a final comparison with experimental results. By the first time, this drug obtained a maximum IE%, determined experimentally by electrochemical impedance spectroscopy measurements at 100 ppm concentration, of about 92.5%, which stands within limits of 1 SD from the predicted ARX model value. From the qualitative perspective, several potential trends have emerged from the estimated values. Among them, macrolides, alkaloids from Rauwolfia species, cephalosporin, and rifamycin antibiotics are expected to exhibit high IE% on steel surfaces. Additionally, IE% increases as the energy of HOMO decreases. The highest efficiency is obtained in case of the molecules with the highest ω and ΔN values. The most efficient drugs are found with pKa ranging from 1.70 to 9.46. The drugs recurrently exhibit aromatic rings, carbonyl, and hydroxyl groups with the highest IE% values.
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Affiliation(s)
- Carlos Beltran-Perez
- Tecnologico de Monterrey, Escuela de Ingeniería y Ciencias, Ave. Eugenio Garza Sada 2501, Monterrey 64849, Mexico; (C.B.-P.); (A.A.A.S.); (G.S.-R.)
| | - Andrés A. A. Serrano
- Tecnologico de Monterrey, Escuela de Ingeniería y Ciencias, Ave. Eugenio Garza Sada 2501, Monterrey 64849, Mexico; (C.B.-P.); (A.A.A.S.); (G.S.-R.)
| | - Gilberto Solís-Rosas
- Tecnologico de Monterrey, Escuela de Ingeniería y Ciencias, Ave. Eugenio Garza Sada 2501, Monterrey 64849, Mexico; (C.B.-P.); (A.A.A.S.); (G.S.-R.)
| | - Anatolio Martínez-Jiménez
- Departamento de Ciencias Básicas, División de CBI (Ciencias Básicas e Ingeniería), Universidad Autónoma Metropolitana, Unidad Azcapotzalco, Área de Física Atómica Molecular Aplicada, San Pablo 180, Ciudad de México 02200, Mexico;
| | - Ricardo Orozco-Cruz
- Unidad Anticorrosión, Instituto de Ingeniería, Universidad Veracruzana, Boca del Río 94292, Mexico;
| | - Araceli Espinoza-Vázquez
- Unidad Anticorrosión, Instituto de Ingeniería, Universidad Veracruzana, Boca del Río 94292, Mexico;
- Correspondence: (A.E.-V.); (A.M.)
| | - Alan Miralrio
- Tecnologico de Monterrey, Escuela de Ingeniería y Ciencias, Ave. Eugenio Garza Sada 2501, Monterrey 64849, Mexico; (C.B.-P.); (A.A.A.S.); (G.S.-R.)
- Correspondence: (A.E.-V.); (A.M.)
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Verboven L, Calders T, Callens S, Black J, Maartens G, Dooley KE, Potgieter S, Warren RM, Laukens K, Van Rie A. A treatment recommender clinical decision support system for personalized medicine: method development and proof-of-concept for drug resistant tuberculosis. BMC Med Inform Decis Mak 2022; 22:56. [PMID: 35236355 PMCID: PMC8892778 DOI: 10.1186/s12911-022-01790-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2021] [Accepted: 02/22/2022] [Indexed: 12/28/2022] Open
Abstract
Background Personalized medicine tailors care based on the patient’s or pathogen’s genotypic and phenotypic characteristics. An automated Clinical Decision Support System (CDSS) could help translate the genotypic and phenotypic characteristics into optimal treatment and thus facilitate implementation of individualized treatment by less experienced physicians.
Methods We developed a hybrid knowledge- and data-driven treatment recommender CDSS. Stakeholders and experts first define the knowledge base by identifying and quantifying drug and regimen features for the prototype model input. In an iterative manner, feedback from experts is harvested to generate model training datasets, machine learning methods are applied to identify complex relations and patterns in the data, and model performance is assessed by estimating the precision at one, mean reciprocal rank and mean average precision. Once the model performance no longer iteratively increases, a validation dataset is used to assess model overfitting. Results We applied the novel methodology to develop a treatment recommender CDSS for individualized treatment of drug resistant tuberculosis as a proof of concept. Using input from stakeholders and three rounds of expert feedback on a dataset of 355 patients with 129 unique drug resistance profiles, the model had a 95% precision at 1 indicating that the highest ranked treatment regimen was considered appropriate by the experts in 95% of cases. Use of a validation data set however suggested substantial model overfitting, with a reduction in precision at 1 to 78%. Conclusion Our novel and flexible hybrid knowledge- and data-driven treatment recommender CDSS is a first step towards the automation of individualized treatment for personalized medicine. Further research should assess its value in fields other than drug resistant tuberculosis, develop solid statistical approaches to assess model performance, and evaluate their accuracy in real-life clinical settings.
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Affiliation(s)
- Lennert Verboven
- Torch Consortium FAMPOP Faculty of Medicine and Health Sciences, University of Antwerp, Antwerp, Belgium. .,ADReM Data Lab, Department of Computer Science, University of Antwerp, Antwerp, Belgium.
| | - Toon Calders
- ADReM Data Lab, Department of Computer Science, University of Antwerp, Antwerp, Belgium
| | - Steven Callens
- Department of Internal Medicine and Infectious Diseases, Ghent University Hospital, Ghent, Belgium
| | - John Black
- Department of Internal Medicine, University of Cape Town and Livingstone Hospital, Port Elizabeth, South Africa
| | - Gary Maartens
- Division of Clinical Pharmacology, Department of Medicine, University of Cape Town, Cape Town, South Africa
| | - Kelly E Dooley
- Divisions of Clinical Pharmacology and Infectious Diseases, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Samantha Potgieter
- Division of Infectious Diseases, Department of Internal Medicine, Faculty of Health Sciences, University of the Free State, Bloemfontein, South Africa
| | - Robin M Warren
- Division of Molecular Biology and Human Genetics, DSI-NRF Centre of Excellence for Biomedical Tuberculosis Research, SAMRC Centre for Tuberculosis Research, Stellenbosch University, Cape Town, South Africa
| | - Kris Laukens
- ADReM Data Lab, Department of Computer Science, University of Antwerp, Antwerp, Belgium
| | - Annelies Van Rie
- Torch Consortium FAMPOP Faculty of Medicine and Health Sciences, University of Antwerp, Antwerp, Belgium
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Sreedevi A, Nitya Harshitha T, Sugumaran V, Shankar P. Application of cognitive computing in healthcare, cybersecurity, big data and IoT: A literature review. Inf Process Manag 2022. [DOI: 10.1016/j.ipm.2022.102888] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
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26
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Svensson AM, Jotterand F. Doctor Ex Machina: A Critical Assessment of the Use of Artificial Intelligence in Health Care. THE JOURNAL OF MEDICINE AND PHILOSOPHY 2022; 47:155-178. [PMID: 35137175 DOI: 10.1093/jmp/jhab036] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023] Open
Abstract
This article examines the potential implications of the implementation of artificial intelligence (AI) in health care for both its delivery and the medical profession. To this end, the first section explores the basic features of AI and the yet theoretical concept of autonomous AI followed by an overview of current and developing AI applications. Against this background, the second section discusses the transforming roles of physicians and changes in the patient-physician relationship that could be a consequence of gradual expansion of AI in health care. Subsequently, an examination of the responsibilities physicians should assume in this process is explored. The third section describes conceivable practical and ethical challenges that implementation of a single all-encompassing AI healthcare system would pose. The fourth section presents arguments for regulation of AI in health care to ensure that these applications do not violate basic ethical principles and that human control of AI will be preserved in the future. In the final section, fundamental components of a moral framework from which such regulation may be derived are brought forward, and some possible strategies for building a moral framework are discussed.
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Affiliation(s)
| | - Fabrice Jotterand
- Medical College of Wisconsin, Milwaukee, Wisconsin, USA.,Universal of Basel, Basel, Switzerland
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27
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Maier-Hein L, Eisenmann M, Sarikaya D, März K, Collins T, Malpani A, Fallert J, Feussner H, Giannarou S, Mascagni P, Nakawala H, Park A, Pugh C, Stoyanov D, Vedula SS, Cleary K, Fichtinger G, Forestier G, Gibaud B, Grantcharov T, Hashizume M, Heckmann-Nötzel D, Kenngott HG, Kikinis R, Mündermann L, Navab N, Onogur S, Roß T, Sznitman R, Taylor RH, Tizabi MD, Wagner M, Hager GD, Neumuth T, Padoy N, Collins J, Gockel I, Goedeke J, Hashimoto DA, Joyeux L, Lam K, Leff DR, Madani A, Marcus HJ, Meireles O, Seitel A, Teber D, Ückert F, Müller-Stich BP, Jannin P, Speidel S. Surgical data science - from concepts toward clinical translation. Med Image Anal 2022; 76:102306. [PMID: 34879287 PMCID: PMC9135051 DOI: 10.1016/j.media.2021.102306] [Citation(s) in RCA: 86] [Impact Index Per Article: 43.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2020] [Revised: 11/03/2021] [Accepted: 11/08/2021] [Indexed: 02/06/2023]
Abstract
Recent developments in data science in general and machine learning in particular have transformed the way experts envision the future of surgery. Surgical Data Science (SDS) is a new research field that aims to improve the quality of interventional healthcare through the capture, organization, analysis and modeling of data. While an increasing number of data-driven approaches and clinical applications have been studied in the fields of radiological and clinical data science, translational success stories are still lacking in surgery. In this publication, we shed light on the underlying reasons and provide a roadmap for future advances in the field. Based on an international workshop involving leading researchers in the field of SDS, we review current practice, key achievements and initiatives as well as available standards and tools for a number of topics relevant to the field, namely (1) infrastructure for data acquisition, storage and access in the presence of regulatory constraints, (2) data annotation and sharing and (3) data analytics. We further complement this technical perspective with (4) a review of currently available SDS products and the translational progress from academia and (5) a roadmap for faster clinical translation and exploitation of the full potential of SDS, based on an international multi-round Delphi process.
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Affiliation(s)
- Lena Maier-Hein
- Division of Computer Assisted Medical Interventions (CAMI), German Cancer Research Center (DKFZ), Heidelberg, Germany; Faculty of Mathematics and Computer Science, Heidelberg University, Heidelberg, Germany; Medical Faculty, Heidelberg University, Heidelberg, Germany.
| | - Matthias Eisenmann
- Division of Computer Assisted Medical Interventions (CAMI), German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Duygu Sarikaya
- Department of Computer Engineering, Faculty of Engineering, Gazi University, Ankara, Turkey; LTSI, Inserm UMR 1099, University of Rennes 1, Rennes, France
| | - Keno März
- Division of Computer Assisted Medical Interventions (CAMI), German Cancer Research Center (DKFZ), Heidelberg, Germany
| | | | - Anand Malpani
- The Malone Center for Engineering in Healthcare, The Johns Hopkins University, Baltimore, Maryland, USA
| | | | - Hubertus Feussner
- Department of Surgery, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Stamatia Giannarou
- The Hamlyn Centre for Robotic Surgery, Imperial College London, London, United Kingdom
| | - Pietro Mascagni
- ICube, University of Strasbourg, CNRS, France; IHU Strasbourg, Strasbourg, France
| | | | - Adrian Park
- Department of Surgery, Anne Arundel Health System, Annapolis, Maryland, USA; Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Carla Pugh
- Department of Surgery, Stanford University School of Medicine, Stanford, California, USA
| | - Danail Stoyanov
- Wellcome/EPSRC Centre for Interventional and Surgical Sciences, University College London, London, United Kingdom
| | - Swaroop S Vedula
- The Malone Center for Engineering in Healthcare, The Johns Hopkins University, Baltimore, Maryland, USA
| | - Kevin Cleary
- The Sheikh Zayed Institute for Pediatric Surgical Innovation, Children's National Hospital, Washington, D.C., USA
| | | | - Germain Forestier
- L'Institut de Recherche en Informatique, Mathématiques, Automatique et Signal (IRIMAS), University of Haute-Alsace, Mulhouse, France; Faculty of Information Technology, Monash University, Clayton, Victoria, Australia
| | - Bernard Gibaud
- LTSI, Inserm UMR 1099, University of Rennes 1, Rennes, France
| | - Teodor Grantcharov
- University of Toronto, Toronto, Ontario, Canada; The Li Ka Shing Knowledge Institute of St. Michael's Hospital, Toronto, Ontario, Canada
| | - Makoto Hashizume
- Kyushu University, Fukuoka, Japan; Kitakyushu Koga Hospital, Fukuoka, Japan
| | - Doreen Heckmann-Nötzel
- Division of Computer Assisted Medical Interventions (CAMI), German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Hannes G Kenngott
- Department for General, Visceral and Transplantation Surgery, Heidelberg University Hospital, Heidelberg, Germany
| | - Ron Kikinis
- Department of Radiology, Brigham and Women's Hospital, and Harvard Medical School, Boston, Massachusetts, USA
| | | | - Nassir Navab
- Computer Aided Medical Procedures, Technical University of Munich, Munich, Germany; Department of Computer Science, The Johns Hopkins University, Baltimore, Maryland, USA
| | - Sinan Onogur
- Division of Computer Assisted Medical Interventions (CAMI), German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Tobias Roß
- Division of Computer Assisted Medical Interventions (CAMI), German Cancer Research Center (DKFZ), Heidelberg, Germany; Medical Faculty, Heidelberg University, Heidelberg, Germany
| | - Raphael Sznitman
- ARTORG Center for Biomedical Engineering Research, University of Bern, Bern, Switzerland
| | - Russell H Taylor
- Department of Computer Science, The Johns Hopkins University, Baltimore, Maryland, USA
| | - Minu D Tizabi
- Division of Computer Assisted Medical Interventions (CAMI), German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Martin Wagner
- Department for General, Visceral and Transplantation Surgery, Heidelberg University Hospital, Heidelberg, Germany
| | - Gregory D Hager
- The Malone Center for Engineering in Healthcare, The Johns Hopkins University, Baltimore, Maryland, USA; Department of Computer Science, The Johns Hopkins University, Baltimore, Maryland, USA
| | - Thomas Neumuth
- Innovation Center Computer Assisted Surgery (ICCAS), University of Leipzig, Leipzig, Germany
| | - Nicolas Padoy
- ICube, University of Strasbourg, CNRS, France; IHU Strasbourg, Strasbourg, France
| | - Justin Collins
- Division of Surgery and Interventional Science, University College London, London, United Kingdom
| | - Ines Gockel
- Department of Visceral, Transplant, Thoracic and Vascular Surgery, Leipzig University Hospital, Leipzig, Germany
| | - Jan Goedeke
- Pediatric Surgery, Dr. von Hauner Children's Hospital, Ludwig-Maximilians-University, Munich, Germany
| | - Daniel A Hashimoto
- University Hospitals Cleveland Medical Center, Case Western Reserve University, Cleveland, Ohio, USA; Surgical AI and Innovation Laboratory, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Luc Joyeux
- My FetUZ Fetal Research Center, Department of Development and Regeneration, Biomedical Sciences, KU Leuven, Leuven, Belgium; Center for Surgical Technologies, Faculty of Medicine, KU Leuven, Leuven, Belgium; Department of Obstetrics and Gynecology, Division Woman and Child, Fetal Medicine Unit, University Hospitals Leuven, Leuven, Belgium; Michael E. DeBakey Department of Surgery, Texas Children's Hospital and Baylor College of Medicine, Houston, Texas, USA
| | - Kyle Lam
- Department of Surgery and Cancer, Imperial College London, London, United Kingdom
| | - Daniel R Leff
- Department of BioSurgery and Surgical Technology, Imperial College London, London, United Kingdom; Hamlyn Centre for Robotic Surgery, Imperial College London, London, United Kingdom; Breast Unit, Imperial Healthcare NHS Trust, London, United Kingdom
| | - Amin Madani
- Department of Surgery, University Health Network, Toronto, Ontario, Canada
| | - Hani J Marcus
- National Hospital for Neurology and Neurosurgery, and UCL Queen Square Institute of Neurology, London, United Kingdom
| | - Ozanan Meireles
- Massachusetts General Hospital, and Harvard Medical School, Boston, Massachusetts, USA
| | - Alexander Seitel
- Division of Computer Assisted Medical Interventions (CAMI), German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Dogu Teber
- Department of Urology, City Hospital Karlsruhe, Karlsruhe, Germany
| | - Frank Ückert
- Institute for Applied Medical Informatics, Hamburg University Hospital, Hamburg, Germany
| | - Beat P Müller-Stich
- Department for General, Visceral and Transplantation Surgery, Heidelberg University Hospital, Heidelberg, Germany
| | - Pierre Jannin
- LTSI, Inserm UMR 1099, University of Rennes 1, Rennes, France
| | - Stefanie Speidel
- Division of Translational Surgical Oncology, National Center for Tumor Diseases (NCT/UCC) Dresden, Dresden, Germany; Centre for Tactile Internet with Human-in-the-Loop (CeTI), TU Dresden, Dresden, Germany
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Lüscher-Dias T, Siqueira Dalmolin RJ, de Paiva Amaral P, Alves TL, Schuch V, Franco GR, Nakaya HI. The evolution of knowledge on genes associated with human diseases. iScience 2022; 25:103610. [PMID: 35005554 PMCID: PMC8719018 DOI: 10.1016/j.isci.2021.103610] [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: 08/20/2021] [Revised: 11/05/2021] [Accepted: 12/08/2021] [Indexed: 12/15/2022] Open
Abstract
Thousands of biomedical scientific articles, including those describing genes associated with human diseases, are published every week. Computational methods such as text mining and machine learning algorithms are now able to automatically detect these associations. In this study, we used a cognitive computing text-mining application to construct a knowledge network comprising 3,723 genes and 99 diseases. We then tracked the yearly changes on these networks to analyze how our knowledge has evolved in the past 30 years. Our systems approach helped to unravel the molecular bases of diseases and detect shared mechanisms between clinically distinct diseases. It also revealed that multi-purpose therapeutic drugs target genes that are commonly associated with several psychiatric, inflammatory, or infectious disorders. By navigating this knowledge tsunami, we were able to extract relevant biological information and insights about human diseases.
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Affiliation(s)
- Thomaz Lüscher-Dias
- Department of Biochemistry and Immunology, Institute of Biological Sciences, Federal University of Minas Gerais, Belo Horizonte, MG, Brazil
| | - Rodrigo Juliani Siqueira Dalmolin
- Bioinformatics Multidisciplinary Environment—BioME, IMD, Federal University of Rio Grande do Norte, Natal, RN, Brazil
- Department of Biochemistry, CB, Federal University of Rio Grande do Norte, Natal, RN, Brazil
| | | | - Tiago Lubiana Alves
- Department of Clinical and Toxicological Analyses, School of Pharmaceutical Sciences, University of São Paulo, São Paulo, Brazil
| | - Viviane Schuch
- Department of Clinical and Toxicological Analyses, School of Pharmaceutical Sciences, University of São Paulo, São Paulo, Brazil
| | - Glória Regina Franco
- Department of Biochemistry and Immunology, Institute of Biological Sciences, Federal University of Minas Gerais, Belo Horizonte, MG, Brazil
| | - Helder I. Nakaya
- Department of Clinical and Toxicological Analyses, School of Pharmaceutical Sciences, University of São Paulo, São Paulo, Brazil
- Scientific Platform Pasteur-University of São Paulo, São Paulo, Brazil
- Hospital Israelita Albert Einstein, São Paulo, Brazil
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Gu X, Sun Z, Zhang W. Composition-driven symptom phrase recognition for Chinese medical consultation corpora. BMC Med Inform Decis Mak 2021; 21:363. [PMID: 34961490 PMCID: PMC8714445 DOI: 10.1186/s12911-021-01716-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2021] [Accepted: 12/08/2021] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Symptom phrase recognition is essential to improve the use of unstructured medical consultation corpora for the development of automated question answering systems. A majority of previous works typically require enough manually annotated training data or as complete a symptom dictionary as possible. However, when applied to real scenarios, they will face a dilemma due to the scarcity of the annotated textual resources and the diversity of the spoken language expressions. METHODS In this paper, we propose a composition-driven method to recognize the symptom phrases from Chinese medical consultation corpora without any annotations. The basic idea is to directly learn models that capture the composition, i.e., the arrangement of the symptom components (semantic units of words). We introduce an automatic annotation strategy for the standard symptom phrases which are collected from multiple data sources. In particular, we combine the position information and the interaction scores between symptom components to characterize the symptom phrases. Equipped with such models, we are allowed to robustly extract symptom phrases that are not seen before. RESULTS Without any manual annotations, our method achieves strong positive results on symptom phrase recognition tasks. Experiments also show that our method enjoys great potential with access to plenty of corpora. CONCLUSIONS Compositionality offers a feasible solution for extracting information from unstructured free text with scarce labels.
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Affiliation(s)
- Xuan Gu
- University of Chinese Academy of Sciences, Beijing, China.,Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Zhengya Sun
- University of Chinese Academy of Sciences, Beijing, China. .,Institute of Automation, Chinese Academy of Sciences, Beijing, China.
| | - Wensheng Zhang
- University of Chinese Academy of Sciences, Beijing, China.,Institute of Automation, Chinese Academy of Sciences, Beijing, China
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30
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Mele C, Russo-Spena T, Marzullo M, Ruggiero A. Boundary work in value co-creation practices: the mediating role of cognitive assistants. JOURNAL OF SERVICE MANAGEMENT 2021. [DOI: 10.1108/josm-10-2020-0381] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
PurposeHow to improve healthcare for the ageing population is attracting academia attention. Emerging technologies (i.e. robots and intelligent agents) look relevant. This paper aims to analyze the role of cognitive assistants as boundary objects in value co-creation practices. We include the perceptions of the main actors – patients, (in)formal caregivers, healthcare professionals – for a fuller network perspective to understand the potential overlap between boundary work and value co-creation practices.Design/methodology/approachWe adopted a grounded approach to gain a contextual understanding design to effectively interpret context and meanings related to human–robot interactions. The study context concerns 21 health solutions that had embedded the Watson cognitive platform and its adoption by the youngest cohort (50–64-year-olds) of the ageing population.FindingsThe cognitive assistant acts as a boundary object by bridging actors, resources and activities. It enacts the boundary work of actors (both ageing and professional, caregivers, families) consisting of four main actions (automated dialoguing, augmented sharing, connected learning and multilayered trusting) that elicit two ageing value co-creation practices: empowering ageing actors in medical care and engaging ageing actors in a healthy lifestyle.Originality/valueWe frame the role of cognitive assistants as boundary objects enabling the boundary work of ageing actors for value co-creation. A cognitive assistant is an “object of activity” that mediates in actors' boundary work by offering novel resource interfaces and widening resource access and resourceness. The boundary work of ageing actors lies in a smarter resource integration that yields broader applications for augmented agency.
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De Silva K, Enticott J, Barton C, Forbes A, Saha S, Nikam R. Use and performance of machine learning models for type 2 diabetes prediction in clinical and community care settings: Protocol for a systematic review and meta-analysis of predictive modeling studies. Digit Health 2021; 7:20552076211047390. [PMID: 34868616 PMCID: PMC8642048 DOI: 10.1177/20552076211047390] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2019] [Accepted: 09/01/2021] [Indexed: 12/23/2022] Open
Abstract
Objective Machine learning involves the use of algorithms without explicit
instructions. Of late, machine learning models have been widely applied for
the prediction of type 2 diabetes. However, no evidence synthesis of the
performance of these prediction models of type 2 diabetes is available. We
aim to identify machine learning prediction models for type 2 diabetes in
clinical and community care settings and determine their predictive
performance. Methods The systematic review of English language machine learning predictive
modeling studies in 12 databases will be conducted. Studies predicting type
2 diabetes in predefined clinical or community settings are eligible.
Standard CHARMS and TRIPOD guidelines will guide data extraction.
Methodological quality will be assessed using a predefined risk of bias
assessment tool. The extent of validation will be categorized by
Reilly–Evans levels. Primary outcomes include model performance metrics of
discrimination ability, calibration, and classification accuracy. Secondary
outcomes include candidate predictors, algorithms used, level of validation,
and intended use of models. The random-effects meta-analysis of c-indices
will be performed to evaluate discrimination abilities. The c-indices will
be pooled per prediction model, per model type, and per algorithm.
Publication bias will be assessed through funnel plots and regression tests.
Sensitivity analysis will be conducted to estimate the effects of study
quality and missing data on primary outcome. The sources of heterogeneity
will be assessed through meta-regression. Subgroup analyses will be
performed for primary outcomes. Ethics and dissemination No ethics approval is required, as no primary or personal data are collected.
Findings will be disseminated through scientific sessions and peer-reviewed
journals. PROSPERO registration number CRD42019130886
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Affiliation(s)
- Kushan De Silva
- Monash Centre for Health Research and Implementation, School of Public Health and Preventive Medicine, Faculty of Medicine, Nursing, and Health Sciences, Monash University, Australia
| | - Joanne Enticott
- Monash Centre for Health Research and Implementation, School of Public Health and Preventive Medicine, Faculty of Medicine, Nursing, and Health Sciences, Monash University, Australia
| | - Christopher Barton
- Department of General Practice, School of Public Health and Preventive Medicine, Faculty of Medicine, Nursing, and Health Sciences, Monash University, Australia
| | - Andrew Forbes
- Biostatistics Unit, Division of Research Methodology, School of Public Health and Preventive Medicine, Faculty of Medicine, Nursing, and Health Sciences, Monash University, Australia
| | - Sajal Saha
- Department of General Practice, School of Public Health and Preventive Medicine, Faculty of Medicine, Nursing, and Health Sciences, Monash University, Australia
| | - Rujuta Nikam
- Department of General Practice, School of Public Health and Preventive Medicine, Faculty of Medicine, Nursing, and Health Sciences, Monash University, Australia
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Pandya S, Thakur A, Saxena S, Jassal N, Patel C, Modi K, Shah P, Joshi R, Gonge S, Kadam K, Kadam P. A Study of the Recent Trends of Immunology: Key Challenges, Domains, Applications, Datasets, and Future Directions. SENSORS (BASEL, SWITZERLAND) 2021; 21:7786. [PMID: 34883787 PMCID: PMC8659723 DOI: 10.3390/s21237786] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/17/2021] [Revised: 11/17/2021] [Accepted: 11/21/2021] [Indexed: 12/13/2022]
Abstract
The human immune system is very complex. Understanding it traditionally required specialized knowledge and expertise along with years of study. However, in recent times, the introduction of technologies such as AIoMT (Artificial Intelligence of Medical Things), genetic intelligence algorithms, smart immunological methodologies, etc., has made this process easier. These technologies can observe relations and patterns that humans do and recognize patterns that are unobservable by humans. Furthermore, these technologies have also enabled us to understand better the different types of cells in the immune system, their structures, their importance, and their impact on our immunity, particularly in the case of debilitating diseases such as cancer. The undertaken study explores the AI methodologies currently in the field of immunology. The initial part of this study explains the integration of AI in healthcare and how it has changed the face of the medical industry. It also details the current applications of AI in the different healthcare domains and the key challenges faced when trying to integrate AI with healthcare, along with the recent developments and contributions in this field by other researchers. The core part of this study is focused on exploring the most common classifications of health diseases, immunology, and its key subdomains. The later part of the study presents a statistical analysis of the contributions in AI in the different domains of immunology and an in-depth review of the machine learning and deep learning methodologies and algorithms that can and have been applied in the field of immunology. We have also analyzed a list of machine learning and deep learning datasets about the different subdomains of immunology. Finally, in the end, the presented study discusses the future research directions in the field of AI in immunology and provides some possible solutions for the same.
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Affiliation(s)
- Sharnil Pandya
- Symbiosis Institute of Technology, Symbiosis International (Deemed) University, Pune 412115, India; (A.T.); (S.S.); (N.J.); (R.J.); (S.G.); (K.K.); (P.K.)
| | - Aanchal Thakur
- Symbiosis Institute of Technology, Symbiosis International (Deemed) University, Pune 412115, India; (A.T.); (S.S.); (N.J.); (R.J.); (S.G.); (K.K.); (P.K.)
| | - Santosh Saxena
- Symbiosis Institute of Technology, Symbiosis International (Deemed) University, Pune 412115, India; (A.T.); (S.S.); (N.J.); (R.J.); (S.G.); (K.K.); (P.K.)
| | - Nandita Jassal
- Symbiosis Institute of Technology, Symbiosis International (Deemed) University, Pune 412115, India; (A.T.); (S.S.); (N.J.); (R.J.); (S.G.); (K.K.); (P.K.)
| | - Chirag Patel
- Computer Science & Engineering, Devang Patel Institute of Advance Technology and Research, Changa 388421, India;
| | - Kirit Modi
- Sankalchand Patel College of Engineering, Sankalchand Patel University, Visnagar 384315, India;
| | - Pooja Shah
- Information Technology Department, Gandhinagar Institute of Technology, Ahmedabad 382010, India;
| | - Rahul Joshi
- Symbiosis Institute of Technology, Symbiosis International (Deemed) University, Pune 412115, India; (A.T.); (S.S.); (N.J.); (R.J.); (S.G.); (K.K.); (P.K.)
| | - Sudhanshu Gonge
- Symbiosis Institute of Technology, Symbiosis International (Deemed) University, Pune 412115, India; (A.T.); (S.S.); (N.J.); (R.J.); (S.G.); (K.K.); (P.K.)
| | - Kalyani Kadam
- Symbiosis Institute of Technology, Symbiosis International (Deemed) University, Pune 412115, India; (A.T.); (S.S.); (N.J.); (R.J.); (S.G.); (K.K.); (P.K.)
| | - Prachi Kadam
- Symbiosis Institute of Technology, Symbiosis International (Deemed) University, Pune 412115, India; (A.T.); (S.S.); (N.J.); (R.J.); (S.G.); (K.K.); (P.K.)
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Chen KS, Menezes K, Rodgers JB, O’Hara DM, Tran N, Fujisawa K, Ishikura S, Khodaei S, Chau H, Cranston A, Kapadia M, Pawar G, Ping S, Krizus A, Lacoste A, Spangler S, Visanji NP, Marras C, Majbour NK, El-Agnaf OMA, Lozano AM, Culotti J, Suo S, Ryu WS, Kalia SK, Kalia LV. Small molecule inhibitors of α-synuclein oligomers identified by targeting early dopamine-mediated motor impairment in C. elegans. Mol Neurodegener 2021; 16:77. [PMID: 34772429 PMCID: PMC8588601 DOI: 10.1186/s13024-021-00497-6] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2021] [Accepted: 10/21/2021] [Indexed: 12/21/2022] Open
Abstract
BACKGROUND Parkinson's disease is a disabling neurodegenerative movement disorder characterized by dopaminergic neuron loss induced by α-synuclein oligomers. There is an urgent need for disease-modifying therapies for Parkinson's disease, but drug discovery is challenged by lack of in vivo models that recapitulate early stages of neurodegeneration. Invertebrate organisms, such as the nematode worm Caenorhabditis elegans, provide in vivo models of human disease processes that can be instrumental for initial pharmacological studies. METHODS To identify early motor impairment of animals expressing α-synuclein in dopaminergic neurons, we first used a custom-built tracking microscope that captures locomotion of single C. elegans with high spatial and temporal resolution. Next, we devised a method for semi-automated and blinded quantification of motor impairment for a population of simultaneously recorded animals with multi-worm tracking and custom image processing. We then used genetic and pharmacological methods to define the features of early motor dysfunction of α-synuclein-expressing C. elegans. Finally, we applied the C. elegans model to a drug repurposing screen by combining it with an artificial intelligence platform and cell culture system to identify small molecules that inhibit α-synuclein oligomers. Screen hits were validated using in vitro and in vivo mammalian models. RESULTS We found a previously undescribed motor phenotype in transgenic α-synuclein C. elegans that correlates with mutant or wild-type α-synuclein protein levels and results from dopaminergic neuron dysfunction, but precedes neuronal loss. Together with artificial intelligence-driven in silico and in vitro screening, this C. elegans model identified five compounds that reduced motor dysfunction induced by α-synuclein. Three of these compounds also decreased α-synuclein oligomers in mammalian neurons, including rifabutin which has not been previously investigated for Parkinson's disease. We found that treatment with rifabutin reduced nigrostriatal dopaminergic neurodegeneration due to α-synuclein in a rat model. CONCLUSIONS We identified a C. elegans locomotor abnormality due to dopaminergic neuron dysfunction that models early α-synuclein-mediated neurodegeneration. Our innovative approach applying this in vivo model to a multi-step drug repurposing screen, with artificial intelligence-driven in silico and in vitro methods, resulted in the discovery of at least one drug that may be repurposed as a disease-modifying therapy for Parkinson's disease.
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Affiliation(s)
- Kevin S. Chen
- Krembil Brain Institute, Toronto Western Hospital, University Health Network, Toronto, ON Canada
| | - Krystal Menezes
- Krembil Brain Institute, Toronto Western Hospital, University Health Network, Toronto, ON Canada
| | | | - Darren M. O’Hara
- Krembil Brain Institute, Toronto Western Hospital, University Health Network, Toronto, ON Canada
| | - Nhat Tran
- Krembil Brain Institute, Toronto Western Hospital, University Health Network, Toronto, ON Canada
| | - Kazuko Fujisawa
- Krembil Brain Institute, Toronto Western Hospital, University Health Network, Toronto, ON Canada
| | - Seiya Ishikura
- Krembil Brain Institute, Toronto Western Hospital, University Health Network, Toronto, ON Canada
| | - Shahin Khodaei
- Donnelly Centre, University of Toronto, Toronto, ON Canada
| | - Hien Chau
- Krembil Brain Institute, Toronto Western Hospital, University Health Network, Toronto, ON Canada
| | - Anna Cranston
- Krembil Brain Institute, Toronto Western Hospital, University Health Network, Toronto, ON Canada
| | - Minesh Kapadia
- Krembil Brain Institute, Toronto Western Hospital, University Health Network, Toronto, ON Canada
| | - Grishma Pawar
- Krembil Brain Institute, Toronto Western Hospital, University Health Network, Toronto, ON Canada
| | - Susan Ping
- Krembil Brain Institute, Toronto Western Hospital, University Health Network, Toronto, ON Canada
| | - Aldis Krizus
- Krembil Brain Institute, Toronto Western Hospital, University Health Network, Toronto, ON Canada
| | | | | | - Naomi P. Visanji
- Edmond J. Safra Program in Parkinson’s Disease and the Morton and Gloria Shulman Movement Disorders Clinic, Division of Neurology, Department of Medicine, Toronto Western Hospital, University Health Network, Toronto, ON Canada
| | - Connie Marras
- Edmond J. Safra Program in Parkinson’s Disease and the Morton and Gloria Shulman Movement Disorders Clinic, Division of Neurology, Department of Medicine, Toronto Western Hospital, University Health Network, Toronto, ON Canada
- Division of Neurology, Department of Medicine, University of Toronto, Toronto, ON Canada
| | - Nour K. Majbour
- Neurological Disorders Research Center, Qatar Biomedical Research Institute (QBRI), Hamad Bin Khalifa University (HBKU), Qatar Foundation, Doha, Qatar
| | - Omar M. A. El-Agnaf
- Neurological Disorders Research Center, Qatar Biomedical Research Institute (QBRI), Hamad Bin Khalifa University (HBKU), Qatar Foundation, Doha, Qatar
| | - Andres M. Lozano
- Krembil Brain Institute, Toronto Western Hospital, University Health Network, Toronto, ON Canada
- Division of Neurosurgery, Department of Surgery, University of Toronto, Toronto, ON Canada
| | - Joseph Culotti
- Lunenfeld-Tanenbaum Research Institute, Mount Sinai Hospital, Toronto, ON Canada
| | - Satoshi Suo
- Lunenfeld-Tanenbaum Research Institute, Mount Sinai Hospital, Toronto, ON Canada
- Department of Pharmacology, Faculty of Medicine, Saitama Medical University, Saitama, Japan
| | - William S. Ryu
- Donnelly Centre, University of Toronto, Toronto, ON Canada
- Department of Physics, University of Toronto, Toronto, ON Canada
| | - Suneil K. Kalia
- Krembil Brain Institute, Toronto Western Hospital, University Health Network, Toronto, ON Canada
- Division of Neurosurgery, Department of Surgery, University of Toronto, Toronto, ON Canada
- KITE and CRANIA, University Health Network, Toronto, ON Canada
| | - Lorraine V. Kalia
- Krembil Brain Institute, Toronto Western Hospital, University Health Network, Toronto, ON Canada
- Edmond J. Safra Program in Parkinson’s Disease and the Morton and Gloria Shulman Movement Disorders Clinic, Division of Neurology, Department of Medicine, Toronto Western Hospital, University Health Network, Toronto, ON Canada
- Division of Neurology, Department of Medicine, University of Toronto, Toronto, ON Canada
- Tanz Centre for Research in Neurodegenerative Diseases, University of Toronto, Toronto, ON Canada
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Iscaro V, Castaldi L, Maresca P, Mazzoni C. Digital transformation in the economics of complexity: the role of predictive models in strategic management. JOURNAL OF STRATEGY AND MANAGEMENT 2021. [DOI: 10.1108/jsma-02-2021-0059] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Purpose
This paper aims to investigate the role of predictive models in the learning and decision-making processes of strategic management. The rapid advancement of digitalisation has contributed to increasing the complexity of the worldwide economy and led to various new competitive dynamics.
Design/methodology/approach
To achieve this purpose, a literature review has been carried out and a predictive model based on Watson, an IBM supercomputer, is presented as a qualitative process model.
Findings
Specific insights derived from a review of the literature highlight organisations' need to modify their decision- and strategy-making processes, which are increasing in speed and frequency, thus also leading to the formulation of emergent and trigger event strategies based on the identification of conditions that require the revision of all or part of the firm's strategy. Predictive models, acting as filters, transform data into informative knowledge that decision-makers can interpret based on individual domain knowledge.
Originality/value
From a theoretical point of view, this paper contributes to the field of digital transformation by proposing the economics of complexity as a paradigm through which to observe and study the issue of predictive models in strategic management. Additionally, the authors analyse the phenomenon from a cognitive perspective, defining the new learning dynamics of digital transformation and the social learning cycle triggered by big data and predictive models. From a managerial and policy-making point of view, this suggests the need to re-shape traditional education contents and dynamics and foster skills that are multi-disciplinary, multi-domain, multi-empathic, multi-interaction and multi-communication between people and things.
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Kanfe SG, Debele GR, Berhanu RD, Ngusie HS, Ahmed MH. Utilisation of district health information system and its associated factors among health professionals working at public health facilities of the southwest of Ethiopia: cross-sectional survey. BMJ Open 2021; 11:e046578. [PMID: 34385240 PMCID: PMC8362697 DOI: 10.1136/bmjopen-2020-046578] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/04/2022] Open
Abstract
OBJECTIVES To assess utilisation of district health information system and its associated factors among health professionals in the southwest of Ethiopia, 2020. SETTING Public health facilities in the southwest of Ethiopia. PARTICIPANTS A facility-based cross-sectional study was conducted among a sample of 260 participants. MAIN OUTCOME MEASURES The main outcome measure was utilisation of the district health information system. RESULTS Overall, 149 (57.3%) of study participants had good utilisation of district health information systems (95% CI 50 to 64.2). Sufficient skills (Adjusted Odds Ratio (AOR) 3.83, 95% CI 1.92 to 7.64), being trained (AOR 3.90, 95% CI 1.95 to 7.79), high motivation (AOR 3.93, 95% CI 1.99 to 7.76), feedback provided (AOR 2.93, 95% CI 1.53 to 5.77) and regular supervision (AOR 3.06, 95% CI 1.56 to 6.01) were associated with utilisation of district health information systems. CONCLUSIONS In general, more than half of the respondents had good utilisation of district health information systems. Providing regular supportive supervision and feedback, having good skills on district health information system use, high motivation and being trained on district health information system will help to bring good utilisation of district health information system for decision making.
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Carracedo-Reboredo P, Liñares-Blanco J, Rodríguez-Fernández N, Cedrón F, Novoa FJ, Carballal A, Maojo V, Pazos A, Fernandez-Lozano C. A review on machine learning approaches and trends in drug discovery. Comput Struct Biotechnol J 2021; 19:4538-4558. [PMID: 34471498 PMCID: PMC8387781 DOI: 10.1016/j.csbj.2021.08.011] [Citation(s) in RCA: 127] [Impact Index Per Article: 42.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2021] [Revised: 08/06/2021] [Accepted: 08/06/2021] [Indexed: 12/30/2022] Open
Abstract
Drug discovery aims at finding new compounds with specific chemical properties for the treatment of diseases. In the last years, the approach used in this search presents an important component in computer science with the skyrocketing of machine learning techniques due to its democratization. With the objectives set by the Precision Medicine initiative and the new challenges generated, it is necessary to establish robust, standard and reproducible computational methodologies to achieve the objectives set. Currently, predictive models based on Machine Learning have gained great importance in the step prior to preclinical studies. This stage manages to drastically reduce costs and research times in the discovery of new drugs. This review article focuses on how these new methodologies are being used in recent years of research. Analyzing the state of the art in this field will give us an idea of where cheminformatics will be developed in the short term, the limitations it presents and the positive results it has achieved. This review will focus mainly on the methods used to model the molecular data, as well as the biological problems addressed and the Machine Learning algorithms used for drug discovery in recent years.
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Key Words
- ADMET, Absorption, distribution, metabolism, elimination and toxicity
- ADR, Adverse Drug Reaction
- AI, Artificial Intelligence
- ANN, Artificial Neural Networks
- APFP, Atom Pairs 2d FingerPrint
- AUC, Area under the Curve
- BBB, Blood–Brain barrier
- CDK, Chemical Development Kit
- CNN, Convolutional Neural Networks
- CNS, Central Nervous System
- CPI, Compound-protein interaction
- CV, Cross Validation
- Cheminformatics
- DL, Deep Learning
- DNA, Deoxyribonucleic acid
- Deep Learning
- Drug Discovery
- ECFP, Extended Connectivity Fingerprints
- FDA, Food and Drug Administration
- FNN, Fully Connected Neural Networks
- FP, Fringerprints
- FS, Feature Selection
- GCN, Graph Convolutional Networks
- GEO, Gene Expression Omnibus
- GNN, Graph Neural Networks
- GO, Gene Ontology
- KEGG, Kyoto Encyclopedia of Genes and Genomes
- MACCS, Molecular ACCess System
- MCC, Matthews correlation coefficient
- MD, Molecular Descriptors
- MKL, Multiple Kernel Learning
- ML, Machine Learning
- Machine Learning
- Molecular Descriptors
- NB, Naive Bayes
- OOB, Out of Bag
- PCA, Principal Component Analyisis
- QSAR
- QSAR, Quantitative structure–activity relationship
- RF, Random Forest
- RNA, Ribonucleic Acid
- SMILES, simplified molecular-input line-entry system
- SVM, Support Vector Machines
- TCGA, The Cancer Genome Atlas
- WHO, World Health Organization
- t-SNE, t-Distributed Stochastic Neighbor Embedding
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Affiliation(s)
- Paula Carracedo-Reboredo
- Department of Computer Science and Information Technologies, Faculty of Computer Science, Universidade da Coruna, Campus Elviña s/n, A Coruña 15071, Spain
| | - Jose Liñares-Blanco
- Department of Computer Science and Information Technologies, Faculty of Computer Science, Universidade da Coruna, Campus Elviña s/n, A Coruña 15071, Spain
- CITIC-Research Center of Information and Communication Technologies, Universidade da Coruna, A Coruña 15071, Spain
| | - Nereida Rodríguez-Fernández
- CITIC-Research Center of Information and Communication Technologies, Universidade da Coruna, A Coruña 15071, Spain
- Department of Computer Science and Information Technologies, Faculty of Communication Science, Universidade da Coruna, Campus Elviña s/n, A Coruña 15071, Spain
| | - Francisco Cedrón
- Department of Computer Science and Information Technologies, Faculty of Computer Science, Universidade da Coruna, Campus Elviña s/n, A Coruña 15071, Spain
| | - Francisco J. Novoa
- Department of Computer Science and Information Technologies, Faculty of Computer Science, Universidade da Coruna, Campus Elviña s/n, A Coruña 15071, Spain
| | - Adrian Carballal
- Department of Computer Science and Information Technologies, Faculty of Computer Science, Universidade da Coruna, Campus Elviña s/n, A Coruña 15071, Spain
- CITIC-Research Center of Information and Communication Technologies, Universidade da Coruna, A Coruña 15071, Spain
- Department of Computer Science and Information Technologies, Faculty of Communication Science, Universidade da Coruna, Campus Elviña s/n, A Coruña 15071, Spain
| | - Victor Maojo
- Biomedical Informatics Group, Artificial Intelligence Department, Polytechnic University of Madrid, Calle de los Ciruelos, Boadilla del Monte, Madrid 28660, Spain
| | - Alejandro Pazos
- Department of Computer Science and Information Technologies, Faculty of Computer Science, Universidade da Coruna, Campus Elviña s/n, A Coruña 15071, Spain
- CITIC-Research Center of Information and Communication Technologies, Universidade da Coruna, A Coruña 15071, Spain
- Grupo de Redes de Neuronas Artificiales y Sistemas Adaptativos. Imagen Médica y Diagnóstico Radiológico (RNASA-IMEDIR), Complexo Hospitalario Universitario de A Coruña (CHUAC), SERGAS, Universidade da Coruña, Instituto de Investigación Biomédica de A Coruña (INIBIC), A Coruña, Spain
| | - Carlos Fernandez-Lozano
- Department of Computer Science and Information Technologies, Faculty of Computer Science, Universidade da Coruna, Campus Elviña s/n, A Coruña 15071, Spain
- CITIC-Research Center of Information and Communication Technologies, Universidade da Coruna, A Coruña 15071, Spain
- Grupo de Redes de Neuronas Artificiales y Sistemas Adaptativos. Imagen Médica y Diagnóstico Radiológico (RNASA-IMEDIR), Complexo Hospitalario Universitario de A Coruña (CHUAC), SERGAS, Universidade da Coruña, Instituto de Investigación Biomédica de A Coruña (INIBIC), A Coruña, Spain
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Rudie JD, Duda J, Duong MT, Chen PH, Xie L, Kurtz R, Ware JB, Choi J, Mattay RR, Botzolakis EJ, Gee JC, Bryan RN, Cook TS, Mohan S, Nasrallah IM, Rauschecker AM. Brain MRI Deep Learning and Bayesian Inference System Augments Radiology Resident Performance. J Digit Imaging 2021; 34:1049-1058. [PMID: 34131794 PMCID: PMC8455800 DOI: 10.1007/s10278-021-00470-1] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2020] [Revised: 04/28/2021] [Accepted: 05/25/2021] [Indexed: 12/15/2022] Open
Abstract
Automated quantitative and probabilistic medical image analysis has the potential to improve the accuracy and efficiency of the radiology workflow. We sought to determine whether AI systems for brain MRI diagnosis could be used as a clinical decision support tool to augment radiologist performance. We utilized previously developed AI systems that combine convolutional neural networks and expert-derived Bayesian networks to distinguish among 50 diagnostic entities on multimodal brain MRIs. We tested whether these systems could augment radiologist performance through an interactive clinical decision support tool known as Adaptive Radiology Interpretation and Education System (ARIES) in 194 test cases. Four radiology residents and three academic neuroradiologists viewed half of the cases unassisted and half with the results of the AI system displayed on ARIES. Diagnostic accuracy of radiologists for top diagnosis (TDx) and top three differential diagnosis (T3DDx) was compared with and without ARIES. Radiology resident performance was significantly better with ARIES for both TDx (55% vs 30%; P < .001) and T3DDx (79% vs 52%; P = 0.002), with the largest improvement for rare diseases (39% increase for T3DDx; P < 0.001). There was no significant difference between attending performance with and without ARIES for TDx (72% vs 69%; P = 0.48) or T3DDx (86% vs 89%; P = 0.39). These findings suggest that a hybrid deep learning and Bayesian inference clinical decision support system has the potential to augment diagnostic accuracy of non-specialists to approach the level of subspecialists for a large array of diseases on brain MRI.
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Affiliation(s)
- Jeffrey D Rudie
- Department of Radiology, Perelman School of Medicine at the University of Pennsylvania, PA, Philadelphia, USA.
- Department of Radiology & Biomedical Imaging, University of California, San Francisco, San Francisco, CA, USA.
| | - Jeffrey Duda
- Department of Radiology, Perelman School of Medicine at the University of Pennsylvania, PA, Philadelphia, USA
| | - Michael Tran Duong
- Department of Radiology, Perelman School of Medicine at the University of Pennsylvania, PA, Philadelphia, USA
| | - Po-Hao Chen
- Department of Radiology, Cleveland Clinic Imaging Institute, Cleveland, OH, USA
| | - Long Xie
- Department of Radiology, Perelman School of Medicine at the University of Pennsylvania, PA, Philadelphia, USA
| | - Robert Kurtz
- Department of Radiology, Perelman School of Medicine at the University of Pennsylvania, PA, Philadelphia, USA
| | - Jeffrey B Ware
- Department of Radiology, Perelman School of Medicine at the University of Pennsylvania, PA, Philadelphia, USA
| | - Joshua Choi
- Department of Radiology, Perelman School of Medicine at the University of Pennsylvania, PA, Philadelphia, USA
| | - Raghav R Mattay
- Department of Radiology, Perelman School of Medicine at the University of Pennsylvania, PA, Philadelphia, USA
| | | | - James C Gee
- Department of Radiology, Perelman School of Medicine at the University of Pennsylvania, PA, Philadelphia, USA
| | - R Nick Bryan
- Department of Diagnostic Medicine, Dell Medical School, University of Texas, Austin, TX, USA
| | - Tessa S Cook
- Department of Radiology, Perelman School of Medicine at the University of Pennsylvania, PA, Philadelphia, USA
| | - Suyash Mohan
- Department of Radiology, Perelman School of Medicine at the University of Pennsylvania, PA, Philadelphia, USA
| | - Ilya M Nasrallah
- Department of Radiology, Perelman School of Medicine at the University of Pennsylvania, PA, Philadelphia, USA
| | - Andreas M Rauschecker
- Department of Radiology, Perelman School of Medicine at the University of Pennsylvania, PA, Philadelphia, USA
- Department of Radiology & Biomedical Imaging, University of California, San Francisco, San Francisco, CA, USA
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Systematic risk identification and assessment using a new risk map in pharmaceutical R&D. Drug Discov Today 2021; 26:2786-2793. [PMID: 34229082 DOI: 10.1016/j.drudis.2021.06.015] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2020] [Revised: 05/21/2021] [Accepted: 06/29/2021] [Indexed: 11/20/2022]
Abstract
Delivering transformative therapies to patients while maintaining growth in the pharmaceutical industry requires an efficient use of research and development (R&D) resources and technologies to develop high-impact new molecular entities (NMEs). However, increasing global R&D competition in the pharmaceutical industry, growing impact of generics and biosimilars, more stringent regulatory requirements, as well as cost-constrained reimbursement frameworks challenge current business models of leading pharmaceutical companies. Big data-based analytics and artificial intelligence (AI) approaches have disrupted various industries and are having an increasing impact in the biopharmaceutical industry, with the promise to improve and accelerate biopharmaceutical R&D processes. Here, we systematically analyze, identify, assess, and categorize key risks across the drug discovery and development value chain using a new risk map approach, providing a comprehensive risk-reward analysis for pharmaceutical R&D.
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The Application of Dynamic Uncertain Causality Graph Based Diagnosis and Treatment Unification Model in the Intelligent Diagnosis and Treatment of Hepatitis B. Symmetry (Basel) 2021. [DOI: 10.3390/sym13071185] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022] Open
Abstract
Although hepatitis B is widespread, it is hard to cure. This paper presents a new and more accurate model for the diagnosis and treatment of hepatitis B. Based on previous research, the diagnosis and treatment modes were combined into one. By adding more influencing factors and risk factors, the overall diagnosis and treatment model will be further expanded, and a richer and more detailed overall diagnosis and treatment model will be constructed. Reverse logic gates are used in the model to improve the accuracy of the treatment planning. The new unified model is more accurate in subdividing diagnosis results, and it is more flexible and accurate in providing dynamic treatment plans. The prediction process and the static diagnosis process of the model are symmetric, and the related sub-graph is symmetric in structure. In addition, an algorithm for predicting the response probability of treatment scheme is developed, so as to predict the subsequent treatment effects of the current treatment scheme, such as the probability of drug resistance. The results show that this method is more accurate than other available systems, and it has encouraging diagnostic accuracy and effectiveness, which provides a promising help for doctors in diagnosing hepatitis B.
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Validation of a Visual-Based Analytics Tool for Outcome Prediction in Polytrauma Patients (WATSON Trauma Pathway Explorer) and Comparison with the Predictive Values of TRISS. J Clin Med 2021; 10:jcm10102115. [PMID: 34068849 PMCID: PMC8153591 DOI: 10.3390/jcm10102115] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2021] [Revised: 05/03/2021] [Accepted: 05/12/2021] [Indexed: 12/23/2022] Open
Abstract
Introduction: Big data-based artificial intelligence (AI) has become increasingly important in medicine and may be helpful in the future to predict diseases and outcomes. For severely injured patients, a new analytics tool has recently been developed (WATSON Trauma Pathway Explorer) to assess individual risk profiles early after trauma. We performed a validation of this tool and a comparison with the Trauma and Injury Severity Score (TRISS), an established trauma survival estimation score. Methods: Prospective data collection, level I trauma centre, 1 January 2018–31 December 2019. Inclusion criteria: Primary admission for trauma, injury severity score (ISS) ≥ 16, age ≥ 16. Parameters: Age, ISS, temperature, presence of head injury by the Glasgow Coma Scale (GCS). Outcomes: SIRS and sepsis within 21 days and early death within 72 h after hospitalisation. Statistics: Area under the receiver operating characteristic (ROC) curve for predictive quality, calibration plots for graphical goodness of fit, Brier score for overall performance of WATSON and TRISS. Results: Between 2018 and 2019, 107 patients were included (33 female, 74 male; mean age 48.3 ± 19.7; mean temperature 35.9 ± 1.3; median ISS 30, IQR 23–36). The area under the curve (AUC) is 0.77 (95% CI 0.68–0.85) for SIRS and 0.71 (95% CI 0.58–0.83) for sepsis. WATSON and TRISS showed similar AUCs to predict early death (AUC 0.90, 95% CI 0.79–0.99 vs. AUC 0.88, 95% CI 0.77–0.97; p = 0.75). The goodness of fit of WATSON (X2 = 8.19, Hosmer–Lemeshow p = 0.42) was superior to that of TRISS (X2 = 31.93, Hosmer–Lemeshow p < 0.05), as was the overall performance based on Brier score (0.06 vs. 0.11 points). Discussion: The validation supports previous reports in terms of feasibility of the WATSON Trauma Pathway Explorer and emphasises its relevance to predict SIRS, sepsis, and early death when compared with the TRISS method.
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Singh G, Papoutsoglou EA, Keijts-Lalleman F, Vencheva B, Rice M, Visser RG, Bachem CW, Finkers R. Extracting knowledge networks from plant scientific literature: potato tuber flesh color as an exemplary trait. BMC PLANT BIOLOGY 2021; 21:198. [PMID: 33894758 PMCID: PMC8070292 DOI: 10.1186/s12870-021-02943-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/16/2020] [Accepted: 03/29/2021] [Indexed: 06/12/2023]
Abstract
BACKGROUND Scientific literature carries a wealth of information crucial for research, but only a fraction of it is present as structured information in databases and therefore can be analyzed using traditional data analysis tools. Natural language processing (NLP) is often and successfully employed to support humans by distilling relevant information from large corpora of free text and structuring it in a way that lends itself to further computational analyses. For this pilot, we developed a pipeline that uses NLP on biological literature to produce knowledge networks. We focused on the flesh color of potato, a well-studied trait with known associations, and we investigated whether these knowledge networks can assist us in formulating new hypotheses on the underlying biological processes. RESULTS We trained an NLP model based on a manually annotated corpus of 34 full-text potato articles, to recognize relevant biological entities and relationships between them in text (genes, proteins, metabolites and traits). This model detected the number of biological entities with a precision of 97.65% and a recall of 88.91% on the training set. We conducted a time series analysis on 4023 PubMed abstract of plant genetics-based articles which focus on 4 major Solanaceous crops (tomato, potato, eggplant and capsicum), to determine that the networks contained both previously known and contemporaneously unknown leads to subsequently discovered biological phenomena relating to flesh color. A novel time-based analysis of these networks indicates a connection between our trait and a candidate gene (zeaxanthin epoxidase) already two years prior to explicit statements of that connection in the literature. CONCLUSIONS Our time-based analysis indicates that network-assisted hypothesis generation shows promise for knowledge discovery, data integration and hypothesis generation in scientific research.
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Affiliation(s)
- Gurnoor Singh
- Plant Breeding, Wageningen University & Research, PO Box 386, Wageningen, 6700AJ The Netherlands
| | | | | | | | - Mark Rice
- IBM Netherlands, Amsterdam, The Netherlands
| | - Richard G.F. Visser
- Plant Breeding, Wageningen University & Research, PO Box 386, Wageningen, 6700AJ The Netherlands
| | - Christian W.B. Bachem
- Plant Breeding, Wageningen University & Research, PO Box 386, Wageningen, 6700AJ The Netherlands
| | - Richard Finkers
- Plant Breeding, Wageningen University & Research, PO Box 386, Wageningen, 6700AJ The Netherlands
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Singh RK, Agrawal S, Sahu A, Kazancoglu Y. Strategic issues of big data analytics applications for managing health-care sector: a systematic literature review and future research agenda. TQM JOURNAL 2021. [DOI: 10.1108/tqm-02-2021-0051] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Abstract
PurposeThe proposed article is aimed at exploring the opportunities, challenges and possible outcomes of incorporating big data analytics (BDA) into health-care sector. The purpose of this study is to find the research gaps in the literature and to investigate the scope of incorporating new strategies in the health-care sector for increasing the efficiency of the system.Design/methodology/approachFora state-of-the-art literature review, a systematic literature review has been carried out to find out research gaps in the field of healthcare using big data (BD) applications. A detailed research methodology including material collection, descriptive analysis and categorization is utilized to carry out the literature review.FindingsBD analysis is rapidly being adopted in health-care sector for utilizing precious information available in terms of BD. However, it puts forth certain challenges that need to be focused upon. The article identifies and explains the challenges thoroughly.Research limitations/implicationsThe proposed study will provide useful guidance to the health-care sector professionals for managing health-care system. It will help academicians and physicians for evaluating, improving and benchmarking the health-care strategies through BDA in the health-care sector. One of the limitations of the study is that it is based on literature review and more in-depth studies may be carried out for the generalization of results.Originality/valueThere are certain effective tools available in the market today that are currently being used by both small and large businesses and corporations. One of them is BD, which may be very useful for health-care sector. A comprehensive literature review is carried out for research papers published between 1974 and 2021.
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Holonic Reengineering to Foster Sustainable Cyber-Physical Systems Design in Cognitive Manufacturing. APPLIED SCIENCES-BASEL 2021. [DOI: 10.3390/app11072941] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Value chain is identified as the generator of the metabolic rift between nature and society. However, the sustainable value chain can mitigate and reverse this rift. In this paper, firstly, a review of the main digital enablers of Industry 4.0 and the current state of cognitive manufacturing is carried out. Secondly, Cyber-Physical Systems are conceived from the holonic paradigm, as an organizational enabler for the whole of enablers. Thirdly, the bijective relationship between holonic paradigm and container-based technology is analyzed. This technology allows mapping the physical and virtual holon as an intelligent agent embodied at the edge, fog and cloud level, with physical and virtual part. Finally, the proposed holonic system based on the cyber-physical holon is developed through multi-agent systems based on container technology. The proposed system allows to model the metabolism of manufacturing systems, from a cell manufacturing to whole value chain, in order to develop, evolve and improve the sustainable value chain.
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Krentel F, Singer F, Rosano-Gonzalez ML, Gibb EA, Liu Y, Davicioni E, Keller N, Stekhoven DJ, Kruithof-de Julio M, Seiler R. A showcase study on personalized in silico drug response prediction based on the genetic landscape of muscle invasive bladder cancer. Sci Rep 2021; 11:5849. [PMID: 33712636 PMCID: PMC7955125 DOI: 10.1038/s41598-021-85151-3] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2020] [Accepted: 02/24/2021] [Indexed: 01/31/2023] Open
Abstract
Improved and cheaper molecular diagnostics allow the shift from "one size fits all" therapies to personalised treatments targeting the individual tumor. However, the wealth of potential targets based on comprehensive sequencing remains a yet unsolved challenge that prevents its routine use in clinical practice. Thus, we designed a workflow that selects the most promising treatment targets based on multi-omics sequencing and in silico drug prediction. In this study we demonstrate the workflow with focus on bladder cancer (BLCA), as there are, to date, no reliable diagnostics available to predict the potential benefit of a therapeutic approach. Within the TCGA-BLCA cohort, our workflow identified a panel of 21 genes and 72 drugs that suggested personalized treatment for 95% of patients-including five genes not yet reported as prognostic markers for clinical testing in BLCA. The automated predictions were complemented by manually curated data, thus allowing for accurate sensitivity- or resistance-directed drug response predictions. We discuss potential improvements of drug-gene interaction databases on the basis of pitfalls that were identified during manual curation.
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Affiliation(s)
| | - Franziska Singer
- NEXUS Personalized Health Technologies, ETH Zurich, Zurich, Switzerland
- SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - María Lourdes Rosano-Gonzalez
- NEXUS Personalized Health Technologies, ETH Zurich, Zurich, Switzerland
- SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | | | - Yang Liu
- GenomeDx Biosciences, Vancouver, Canada
| | | | | | - Daniel J Stekhoven
- NEXUS Personalized Health Technologies, ETH Zurich, Zurich, Switzerland
- SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Marianna Kruithof-de Julio
- Department of Urology, University of Bern, 3010, Bern, Switzerland
- Department for BioMedical Research, Urology Research Laboratory, University of Bern, Bern, Switzerland
- Translational Organoid Research, Department for BioMedical Research, University of Bern, Bern, Switzerland
- Bern Center for Precision Medicine, University of Bern, Bern University Hospital, Bern, Switzerland
| | - Roland Seiler
- Department of Urology, University of Bern, 3010, Bern, Switzerland.
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van Biesen W, Van Der Straeten C, Sterckx S, Steen J, Diependaele L, Decruyenaere J. The concept of justifiable healthcare and how big data can help us to achieve it. BMC Med Inform Decis Mak 2021; 21:87. [PMID: 33676513 PMCID: PMC7937275 DOI: 10.1186/s12911-021-01444-7] [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: 07/02/2020] [Accepted: 02/16/2021] [Indexed: 01/08/2023] Open
Abstract
Over the last decades, the face of health care has changed dramatically, with big improvements in what is technically feasible. However, there are indicators that the current approach to evaluating evidence in health care is not holistic and hence in the long run, health care will not be sustainable. New conceptual and normative frameworks for the evaluation of health care need to be developed and investigated. The current paper presents a novel framework of justifiable health care and explores how the use of artificial intelligence and big data can contribute to achieving the goals of this framework.
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Affiliation(s)
- Wim van Biesen
- Renal Division, 0K12 IA, Ghent University Hospital, Corneel Heymanslaan 10, 9000, Gent, Belgium.
- Consortium for Justifiable Healthcare, Ghent University Hospital, Ghent, Belgium.
| | | | - Sigrid Sterckx
- Consortium for Justifiable Healthcare, Ghent University Hospital, Ghent, Belgium
- Bioethics Institute Ghent, Department of Philosophy and Moral Sciences, Ghent University, Ghent, Belgium
| | - Johan Steen
- Renal Division, 0K12 IA, Ghent University Hospital, Corneel Heymanslaan 10, 9000, Gent, Belgium
- Consortium for Justifiable Healthcare, Ghent University Hospital, Ghent, Belgium
| | - Lisa Diependaele
- Consortium for Justifiable Healthcare, Ghent University Hospital, Ghent, Belgium
- Bioethics Institute Ghent, Department of Philosophy and Moral Sciences, Ghent University, Ghent, Belgium
| | - Johan Decruyenaere
- Consortium for Justifiable Healthcare, Ghent University Hospital, Ghent, Belgium
- Department of Intensive Care, Ghent University Hospital, Ghent, Belgium
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Cognitive analysis of metabolomics data for systems biology. Nat Protoc 2021; 16:1376-1418. [PMID: 33483720 DOI: 10.1038/s41596-020-00455-4] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2020] [Accepted: 10/27/2020] [Indexed: 01/30/2023]
Abstract
Cognitive computing is revolutionizing the way big data are processed and integrated, with artificial intelligence (AI) natural language processing (NLP) platforms helping researchers to efficiently search and digest the vast scientific literature. Most available platforms have been developed for biomedical researchers, but new NLP tools are emerging for biologists in other fields and an important example is metabolomics. NLP provides literature-based contextualization of metabolic features that decreases the time and expert-level subject knowledge required during the prioritization, identification and interpretation steps in the metabolomics data analysis pipeline. Here, we describe and demonstrate four workflows that combine metabolomics data with NLP-based literature searches of scientific databases to aid in the analysis of metabolomics data and their biological interpretation. The four procedures can be used in isolation or consecutively, depending on the research questions. The first, used for initial metabolite annotation and prioritization, creates a list of metabolites that would be interesting for follow-up. The second workflow finds literature evidence of the activity of metabolites and metabolic pathways in governing the biological condition on a systems biology level. The third is used to identify candidate biomarkers, and the fourth looks for metabolic conditions or drug-repurposing targets that the two diseases have in common. The protocol can take 1-4 h or more to complete, depending on the processing time of the various software used.
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Karapiperis C, Chasapi A, Angelis L, Scouras ZG, Mastroberardino PG, Tapio S, Atkinson MJ, Ouzounis CA. The Coming of Age for Big Data in Systems Radiobiology, an Engineering Perspective. BIG DATA 2021; 9:63-71. [PMID: 32991205 DOI: 10.1089/big.2019.0144] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
As high-throughput approaches in biological and biomedical research are transforming the life sciences into information-driven disciplines, modern analytics platforms for big data have started to address the needs for efficient and systematic data analysis and interpretation. We observe that radiobiology is following this general trend, with -omics information providing unparalleled depth into the biomolecular mechanisms of radiation response-defined as systems radiobiology. We outline the design of computational frameworks and discuss the analysis of big data in low-dose ionizing radiation (LDIR) responses of the mammalian brain. Following successful examples and best practices of approaches for the analysis of big data in life sciences and health care, we present the needs and requirements for radiation research. Our goal is to raise awareness for the radiobiology community about the new technological possibilities that can capture complex information and execute data analytics on a large scale. The production of large data sets from genome-wide experiments (quantity) and the complexity of radiation research with multidimensional experimental designs (quality) will necessitate the adoption of latest information technologies. The main objective was to translate research results into applied clinical and epidemiological practice and understand the responses of biological tissues to LDIR to define new radiation protection policies. We envisage a future where multidisciplinary teams include data scientists, artificial intelligence experts, DevOps engineers, and of course radiation experts to fulfill the augmented needs of the radiobiology community, accelerate research, and devise new strategies.
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Affiliation(s)
- Christos Karapiperis
- School of Informatics, Aristotle University of Thessalonica (AUTH), Thessalonica, Greece
| | - Anastasia Chasapi
- Biological Computation & Process Laboratory (BCPL), Chemical Process & Energy Resources Institute (CPERI), Centre for Research & Technology Hellas (CERTH), Thessalonica, Greece
| | - Lefteris Angelis
- School of Informatics, Aristotle University of Thessalonica (AUTH), Thessalonica, Greece
| | - Zacharias G Scouras
- School of Biology, Aristotle University of Thessalonica (AUTH), Thessalonica, Greece
| | | | - Soile Tapio
- Institute of Radiation Biology, Helmholtz Zentrum Muenchen, German Research Center for Environmental Health (HMGU), Neuherberg, Germany
| | - Michael J Atkinson
- Institute of Radiation Biology, Helmholtz Zentrum Muenchen, German Research Center for Environmental Health (HMGU), Neuherberg, Germany
| | - Christos A Ouzounis
- School of Informatics, Aristotle University of Thessalonica (AUTH), Thessalonica, Greece
- Biological Computation & Process Laboratory (BCPL), Chemical Process & Energy Resources Institute (CPERI), Centre for Research & Technology Hellas (CERTH), Thessalonica, Greece
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Kimmig J, Zechel S, Schubert US. Digital Transformation in Materials Science: A Paradigm Change in Material's Development. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2021; 33:e2004940. [PMID: 33410218 DOI: 10.1002/adma.202004940] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/20/2020] [Revised: 09/01/2020] [Indexed: 06/12/2023]
Abstract
The ongoing digitalization is rapidly changing and will further revolutionize all parts of life. This statement is currently omnipresent in the media as well as in the scientific community; however, the exact consequences of the proceeding digitalization for the field of materials science in general and the way research will be performed in the future are still unclear. There are first promising examples featuring the potential to change discovery and development approaches toward new materials. Nevertheless, a wide range of open questions have to be solved in order to enable the so-called digital-supported material research. The current state-of-the-art, the present and future challenges, as well as the resulting perspectives for materials science are described.
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Affiliation(s)
- Julian Kimmig
- Laboratory of Organic and Macromolecular Chemistry (IOMC), Friedrich Schiller University Jena, Humboldtstr. 10, Jena, 07743, Germany
- Jena Center for Soft Matter (JCSM), Friedrich Schiller University Jena, Philosophenweg 7, Jena, 07743, Germany
| | - Stefan Zechel
- Laboratory of Organic and Macromolecular Chemistry (IOMC), Friedrich Schiller University Jena, Humboldtstr. 10, Jena, 07743, Germany
- Jena Center for Soft Matter (JCSM), Friedrich Schiller University Jena, Philosophenweg 7, Jena, 07743, Germany
| | - Ulrich S Schubert
- Laboratory of Organic and Macromolecular Chemistry (IOMC), Friedrich Schiller University Jena, Humboldtstr. 10, Jena, 07743, Germany
- Jena Center for Soft Matter (JCSM), Friedrich Schiller University Jena, Philosophenweg 7, Jena, 07743, Germany
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Rodrigues JF, Florea L, de Oliveira MCF, Diamond D, Oliveira ON. Big data and machine learning for materials science. DISCOVER MATERIALS 2021; 1:12. [PMID: 33899049 PMCID: PMC8054236 DOI: 10.1007/s43939-021-00012-0] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/09/2021] [Accepted: 04/01/2021] [Indexed: 05/11/2023]
Abstract
Herein, we review aspects of leading-edge research and innovation in materials science that exploit big data and machine learning (ML), two computer science concepts that combine to yield computational intelligence. ML can accelerate the solution of intricate chemical problems and even solve problems that otherwise would not be tractable. However, the potential benefits of ML come at the cost of big data production; that is, the algorithms demand large volumes of data of various natures and from different sources, from material properties to sensor data. In the survey, we propose a roadmap for future developments with emphasis on computer-aided discovery of new materials and analysis of chemical sensing compounds, both prominent research fields for ML in the context of materials science. In addition to providing an overview of recent advances, we elaborate upon the conceptual and practical limitations of big data and ML applied to materials science, outlining processes, discussing pitfalls, and reviewing cases of success and failure.
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Affiliation(s)
- Jose F. Rodrigues
- Institute of Mathematical Sciences and Computing, University of São Paulo (USP), São Carlos, SP Brazil
| | - Larisa Florea
- SFI Research Centre for Advanced Materials and BioEngineering Research Trinity College Dublin, The University of Dublin, Dublin, Ireland
| | - Maria C. F. de Oliveira
- Institute of Mathematical Sciences and Computing, University of São Paulo (USP), São Carlos, SP Brazil
| | - Dermot Diamond
- Insight Centre for Data Analytics, National Centre for Sensor Research, Dublin City University, Dublin 9, Dublin, Ireland
| | - Osvaldo N. Oliveira
- São Carlos Institute of Physics, University of São Paulo (USP), São Carlos, SP Brazil
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Zhang Y, Sheng M, Zhou R, Wang Y, Han G, Zhang H, Xing C, Dong J. HKGB: An Inclusive, Extensible, Intelligent, Semi-auto-constructed Knowledge Graph Framework for Healthcare with Clinicians’ Expertise Incorporated. Inf Process Manag 2020. [DOI: 10.1016/j.ipm.2020.102324] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
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