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Schultze JL. Building Trust in Medical Use of Artificial Intelligence – The Swarm Learning Principle. JOURNAL OF CME 2023; 12:2162202. [PMID: 36969482 PMCID: PMC10031775 DOI: 10.1080/28338073.2022.2162202] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/12/2023]
Abstract
An avalanche of medical data is starting to be build up. With the digitalisation of medicine and novel approaches such as the omics technologies, we are conquering ever bigger data spaces to be used to describe pathophysiology of diseases, define biomarkers for diagnostic purposes or identify novel drug targets. Utilising this growing lake of medical data will only be possible, if we make use of machine learning, in particular artificial intelligence (AI)-based algorithms. While the technological developments and chances of the data and information sciences are enormous, the use of AI in medicine also bears challenges and many of the current information technologies (IT) do not follow established medical traditions of mentoring, learning together, sharing insights, while preserving patient's data privacy by patient physician privilege. Other challenges to the medical sector are demands from the scientific community such as "Open Science", "Open Data", "Open Access" principles. A major question to be solved is how to guide technological developments in the IT sector to serve well-established medical traditions and processes, yet allow medicine to benefit from the many advantages of state-of-the-art IT. Here, I provide the Swarm Learning (SL) principle as a conceptual framework designed to foster medical standards, processes and traditions. A major difference to current IT solutions is the inherent property of SL to appreciate and acknowledge existing regulations in medicine that have been proven beneficial for patients and medical personal alike for centuries.
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Affiliation(s)
- Joachim L. Schultze
- Systems Medicine, Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE), Bonn, Germany
- PRECISE Platform for Single Cell Genomics and Epigenomics, Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE) and University of Bonn, Bonn, Germany
- Genomics & Immunoregulation, sLife and Medical Sciences (LIMES) Institute, University of Bonn, Bonn, Germany
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Taneva SG, Todinova S, Andreeva T. Morphometric and Nanomechanical Screening of Peripheral Blood Cells with Atomic Force Microscopy for Label-Free Assessment of Alzheimer's Disease, Parkinson's Disease, and Amyotrophic Lateral Sclerosis. Int J Mol Sci 2023; 24:14296. [PMID: 37762599 PMCID: PMC10531602 DOI: 10.3390/ijms241814296] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2023] [Revised: 09/09/2023] [Accepted: 09/16/2023] [Indexed: 09/29/2023] Open
Abstract
Neurodegenerative disorders (NDDs) are complex, multifactorial disorders with significant social and economic impact in today's society. NDDs are predicted to become the second-most common cause of death in the next few decades due to an increase in life expectancy but also to a lack of early diagnosis and mainly symptomatic treatment. Despite recent advances in diagnostic and therapeutic methods, there are yet no reliable biomarkers identifying the complex pathways contributing to these pathologies. The development of new approaches for early diagnosis and new therapies, together with the identification of non-invasive and more cost-effective diagnostic biomarkers, is one of the main trends in NDD biomedical research. Here we summarize data on peripheral biomarkers, biofluids (cerebrospinal fluid and blood plasma), and peripheral blood cells (platelets (PLTs) and red blood cells (RBCs)), reported so far for the three most common NDDs-Alzheimer's disease (AD), Parkinson's disease (PD), and amyotrophic lateral sclerosis (ALS). PLTs and RBCs, beyond their primary physiological functions, are increasingly recognized as valuable sources of biomarkers for NDDs. Special attention is given to the morphological and nanomechanical signatures of PLTs and RBCs as biophysical markers for the three pathologies. Modifications of the surface nanostructure and morphometric and nanomechanical signatures of PLTs and RBCs from patients with AD, PD, and ALS have been revealed by atomic force microscopy (AFM). AFM is currently experiencing rapid and widespread adoption in biomedicine and clinical medicine, in particular for early diagnostics of various medical conditions. AFM is a unique instrument without an analog, allowing the generation of three-dimensional cell images with extremely high spatial resolution at near-atomic scale, which are complemented by insights into the mechanical properties of cells and subcellular structures. Data demonstrate that AFM can distinguish between the three pathologies and the normal, healthy state. The specific PLT and RBC signatures can serve as biomarkers in combination with the currently used diagnostic tools. We highlight the strong correlation of the morphological and nanomechanical signatures between RBCs and PLTs in PD, ALS, and AD.
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Affiliation(s)
- Stefka G. Taneva
- Institute of Biophysics and Biomedical Engineering, Bulgarian Academy of Sciences, “Acad. G. Bontchev” Str. 21, 1113 Sofia, Bulgaria; (S.T.); (T.A.)
| | - Svetla Todinova
- Institute of Biophysics and Biomedical Engineering, Bulgarian Academy of Sciences, “Acad. G. Bontchev” Str. 21, 1113 Sofia, Bulgaria; (S.T.); (T.A.)
| | - Tonya Andreeva
- Institute of Biophysics and Biomedical Engineering, Bulgarian Academy of Sciences, “Acad. G. Bontchev” Str. 21, 1113 Sofia, Bulgaria; (S.T.); (T.A.)
- Faculty of Life Sciences, Reutlingen University, Alteburgstraße 150, D-72762 Reutlingen, Germany
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Liu G, Lu W, Qiu J, Shi L. Identifying individuals with attention‐deficit/hyperactivity disorder based on multisite resting‐state functional magnetic resonance imaging: A radiomics analysis. Hum Brain Mapp 2023; 44:3433-3445. [PMID: 36971664 PMCID: PMC10171499 DOI: 10.1002/hbm.26290] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2021] [Revised: 12/17/2022] [Accepted: 03/13/2023] [Indexed: 03/29/2023] Open
Abstract
Attention-deficit/hyperactivity disorder (ADHD) is one of the most common neurodevelopmental disorders, characterized by symptoms of age-inappropriate inattention, hyperactivity, and impulsivity. Apart from behavioral symptoms investigated by psychiatric methods, there is no standard biological test to diagnose ADHD. This study aimed to explore whether the radiomics features based on resting-state functional magnetic resonance (rs-fMRI) have more discriminative power for the diagnosis of ADHD. The rs-fMRI of 187 subjects with ADHD and 187 healthy controls were collected from 5 sites of ADHD-200 Consortium. A total of four preprocessed rs-fMRI images including regional homogeneity (ReHo), amplitude of low-frequency fluctuation (ALFF), voxel-mirrored homotopic connectivity (VMHC) and network degree centrality (DC) were used in this study. From each of the four images, we extracted 93 radiomics features within each of 116 automated anatomical labeling brain areas, resulting in a total of 43,152 features for each subject. After dimension reduction and feature selection, 19 radiomics features were retained (5 from ALFF, 9 from ReHo, 3 from VMHC and 2 from DC). By training and optimizing a support vector machine model using the retained features of training dataset, we achieved the accuracy of 76.3% and 77.0% (areas under curve = 0.811 and 0.797) in the training and testing datasets, respectively. Our findings demonstrate that radiomics can be a novel strategy for fully utilizing rs-fMRI information to distinguish ADHD from healthy controls. The rs-fMRI-based radiomics features have the potential to be neuroimaging biomarkers for ADHD.
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Segura T, Medrano IH, Collazo S, Maté C, Sguera C, Del Rio-Bermudez C, Casero H, Salcedo I, García-García J, Alcahut-Rodríguez C, Taberna M. Symptoms timeline and outcomes in amyotrophic lateral sclerosis using artificial intelligence. Sci Rep 2023; 13:702. [PMID: 36639403 PMCID: PMC9839769 DOI: 10.1038/s41598-023-27863-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2022] [Accepted: 01/09/2023] [Indexed: 01/15/2023] Open
Abstract
Amyotrophic lateral sclerosis (ALS) is a fatal, neurodegenerative motor neuron disease. Although an early diagnosis is crucial to provide adequate care and improve survival, patients with ALS experience a significant diagnostic delay. This study aimed to use real-world data to describe the clinical profile and timing between symptom onset, diagnosis, and relevant outcomes in ALS. Retrospective and multicenter study in 5 representative hospitals and Primary Care services in the SESCAM Healthcare Network (Castilla-La Mancha, Spain). Using Natural Language Processing (NLP), the clinical information in electronic health records of all patients with ALS was extracted between January 2014 and December 2018. From a source population of all individuals attended in the participating hospitals, 250 ALS patients were identified (61.6% male, mean age 64.7 years). Of these, 64% had spinal and 36% bulbar ALS. For most defining symptoms, including dyspnea, dysarthria, dysphagia and fasciculations, the overall diagnostic delay from symptom onset was 11 (6-18) months. Prior to diagnosis, only 38.8% of patients had visited the neurologist. In a median post-diagnosis follow-up of 25 months, 52% underwent gastrostomy, 64% non-invasive ventilation, 16.4% tracheostomy, and 87.6% riluzole treatment; these were more commonly reported (all Ps < 0.05) and showed greater probability of occurrence (all Ps < 0.03) in bulbar ALS. Our results highlight the diagnostic delay in ALS and revealed differences in the clinical characteristics and occurrence of major disease-specific events across ALS subtypes. NLP holds great promise for its application in the wider context of rare neurological diseases.
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Affiliation(s)
- Tomás Segura
- University Hospital of Albacete, Albacete, Spain.
| | | | | | | | - Carlo Sguera
- Savana Research, Madrid, Spain.,UC3M-Santander Big Data Institute, Madrid, Spain
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Warnat-Herresthal S, Schultze H, Shastry KL, Manamohan S, Mukherjee S, Garg V, Sarveswara R, Händler K, Pickkers P, Aziz NA, Ktena S, Tran F, Bitzer M, Ossowski S, Casadei N, Herr C, Petersheim D, Behrends U, Kern F, Fehlmann T, Schommers P, Lehmann C, Augustin M, Rybniker J, Altmüller J, Mishra N, Bernardes JP, Krämer B, Bonaguro L, Schulte-Schrepping J, De Domenico E, Siever C, Kraut M, Desai M, Monnet B, Saridaki M, Siegel CM, Drews A, Nuesch-Germano M, Theis H, Heyckendorf J, Schreiber S, Kim-Hellmuth S, Nattermann J, Skowasch D, Kurth I, Keller A, Bals R, Nürnberg P, Rieß O, Rosenstiel P, Netea MG, Theis F, Mukherjee S, Backes M, Aschenbrenner AC, Ulas T, Breteler MMB, Giamarellos-Bourboulis EJ, Kox M, Becker M, Cheran S, Woodacre MS, Goh EL, Schultze JL. Swarm Learning for decentralized and confidential clinical machine learning. Nature 2021; 594:265-270. [PMID: 34040261 PMCID: PMC8189907 DOI: 10.1038/s41586-021-03583-3] [Citation(s) in RCA: 183] [Impact Index Per Article: 61.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2020] [Accepted: 04/26/2021] [Indexed: 01/08/2023]
Abstract
Fast and reliable detection of patients with severe and heterogeneous illnesses is a major goal of precision medicine1,2. Patients with leukaemia can be identified using machine learning on the basis of their blood transcriptomes3. However, there is an increasing divide between what is technically possible and what is allowed, because of privacy legislation4,5. Here, to facilitate the integration of any medical data from any data owner worldwide without violating privacy laws, we introduce Swarm Learning—a decentralized machine-learning approach that unites edge computing, blockchain-based peer-to-peer networking and coordination while maintaining confidentiality without the need for a central coordinator, thereby going beyond federated learning. To illustrate the feasibility of using Swarm Learning to develop disease classifiers using distributed data, we chose four use cases of heterogeneous diseases (COVID-19, tuberculosis, leukaemia and lung pathologies). With more than 16,400 blood transcriptomes derived from 127 clinical studies with non-uniform distributions of cases and controls and substantial study biases, as well as more than 95,000 chest X-ray images, we show that Swarm Learning classifiers outperform those developed at individual sites. In addition, Swarm Learning completely fulfils local confidentiality regulations by design. We believe that this approach will notably accelerate the introduction of precision medicine. Swarm Learning is a decentralized machine learning approach that outperforms classifiers developed at individual sites for COVID-19 and other diseases while preserving confidentiality and privacy.
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Affiliation(s)
- Stefanie Warnat-Herresthal
- Systems Medicine, Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE), Bonn, Germany.,Genomics and Immunoregulation, Life & Medical Sciences (LIMES) Institute, University of Bonn, Bonn, Germany
| | | | | | | | | | - Vishesh Garg
- Hewlett Packard Enterprise, Houston, TX, USA.,Mesh Dynamics, Bangalore, India
| | | | - Kristian Händler
- Systems Medicine, Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE), Bonn, Germany.,PRECISE Platform for Single Cell Genomics and Epigenomics, Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE) and the University of Bonn, Bonn, Germany
| | - Peter Pickkers
- Department of Intensive Care Medicine and Radboud Center for Infectious Diseases (RCI), Radboud University Medical Center, Nijmegen, The Netherlands
| | - N Ahmad Aziz
- Population Health Sciences, Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE), Bonn, Germany.,Department of Neurology, Faculty of Medicine, University of Bonn, Bonn, Germany
| | - Sofia Ktena
- 4th Department of Internal Medicine, National and Kapodistrian University of Athens, Medical School, Athens, Greece
| | - Florian Tran
- Department of Internal Medicine I, Christian-Albrechts-University and University Hospital Schleswig-Holstein, Kiel, Germany.,Institute of Clinical Molecular Biology, Christian-Albrechts-University and University Hospital Schleswig-Holstein, Kiel, Germany
| | - Michael Bitzer
- Department of Internal Medicine I, University Hospital, University of Tübingen, Tübingen, Germany
| | - Stephan Ossowski
- Institute of Medical Genetics and Applied Genomics, University of Tübingen, Tübingen, Germany.,NGS Competence Center Tübingen, Tübingen, Germany
| | - Nicolas Casadei
- Institute of Medical Genetics and Applied Genomics, University of Tübingen, Tübingen, Germany.,NGS Competence Center Tübingen, Tübingen, Germany
| | - Christian Herr
- Department of Internal Medicine V, Saarland University Hospital, Homburg, Germany
| | - Daniel Petersheim
- Department of Pediatrics, Dr. von Hauner Children's Hospital, University Hospital LMU Munich, Munich, Germany
| | - Uta Behrends
- Children's Hospital, Medical Faculty, Technical University Munich, Munich, Germany
| | - Fabian Kern
- Clinical Bioinformatics, Saarland University, Saarbrücken, Germany
| | - Tobias Fehlmann
- Clinical Bioinformatics, Saarland University, Saarbrücken, Germany
| | - Philipp Schommers
- Department I of Internal Medicine, Faculty of Medicine and University Hospital of Cologne, University of Cologne, Cologne, Germany
| | - Clara Lehmann
- Department I of Internal Medicine, Faculty of Medicine and University Hospital of Cologne, University of Cologne, Cologne, Germany.,Center for Molecular Medicine Cologne (CMMC), University of Cologne, Cologne, Germany.,German Center for Infection Research (DZIF), Partner Site Bonn-Cologne, Cologne, Germany
| | - Max Augustin
- Department I of Internal Medicine, Faculty of Medicine and University Hospital of Cologne, University of Cologne, Cologne, Germany.,Center for Molecular Medicine Cologne (CMMC), University of Cologne, Cologne, Germany.,German Center for Infection Research (DZIF), Partner Site Bonn-Cologne, Cologne, Germany
| | - Jan Rybniker
- Department I of Internal Medicine, Faculty of Medicine and University Hospital of Cologne, University of Cologne, Cologne, Germany.,Center for Molecular Medicine Cologne (CMMC), University of Cologne, Cologne, Germany.,German Center for Infection Research (DZIF), Partner Site Bonn-Cologne, Cologne, Germany
| | - Janine Altmüller
- Cologne Center for Genomics, West German Genome Center, University of Cologne, Cologne, Germany
| | - Neha Mishra
- Institute of Clinical Molecular Biology, Christian-Albrechts-University and University Hospital Schleswig-Holstein, Kiel, Germany
| | - Joana P Bernardes
- Institute of Clinical Molecular Biology, Christian-Albrechts-University and University Hospital Schleswig-Holstein, Kiel, Germany
| | - Benjamin Krämer
- Clinical Infectious Diseases, Research Center Borstel and German Center for Infection Research (DZIF), Partner Site Hamburg-Lübeck-Borstel-Riems, Borstel, Germany
| | - Lorenzo Bonaguro
- Systems Medicine, Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE), Bonn, Germany.,Genomics and Immunoregulation, Life & Medical Sciences (LIMES) Institute, University of Bonn, Bonn, Germany
| | - Jonas Schulte-Schrepping
- Systems Medicine, Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE), Bonn, Germany.,Genomics and Immunoregulation, Life & Medical Sciences (LIMES) Institute, University of Bonn, Bonn, Germany
| | - Elena De Domenico
- Systems Medicine, Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE), Bonn, Germany.,PRECISE Platform for Single Cell Genomics and Epigenomics, Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE) and the University of Bonn, Bonn, Germany
| | | | - Michael Kraut
- Systems Medicine, Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE), Bonn, Germany.,PRECISE Platform for Single Cell Genomics and Epigenomics, Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE) and the University of Bonn, Bonn, Germany
| | | | | | - Maria Saridaki
- 4th Department of Internal Medicine, National and Kapodistrian University of Athens, Medical School, Athens, Greece
| | | | - Anna Drews
- Systems Medicine, Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE), Bonn, Germany.,PRECISE Platform for Single Cell Genomics and Epigenomics, Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE) and the University of Bonn, Bonn, Germany
| | - Melanie Nuesch-Germano
- Systems Medicine, Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE), Bonn, Germany.,Genomics and Immunoregulation, Life & Medical Sciences (LIMES) Institute, University of Bonn, Bonn, Germany
| | - Heidi Theis
- Systems Medicine, Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE), Bonn, Germany.,PRECISE Platform for Single Cell Genomics and Epigenomics, Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE) and the University of Bonn, Bonn, Germany
| | - Jan Heyckendorf
- Clinical Infectious Diseases, Research Center Borstel and German Center for Infection Research (DZIF), Partner Site Hamburg-Lübeck-Borstel-Riems, Borstel, Germany
| | - Stefan Schreiber
- Department of Internal Medicine I, Christian-Albrechts-University and University Hospital Schleswig-Holstein, Kiel, Germany
| | - Sarah Kim-Hellmuth
- Department of Pediatrics, Dr. von Hauner Children's Hospital, University Hospital LMU Munich, Munich, Germany
| | | | - Jacob Nattermann
- Department of Internal Medicine I, University Hospital Bonn, Bonn, Germany.,German Center for Infection Research (DZIF), Braunschweig, Germany
| | - Dirk Skowasch
- Department of Internal Medicine II - Cardiology/Pneumology, University of Bonn, Bonn, Germany
| | - Ingo Kurth
- Institute of Human Genetics, Medical Faculty, RWTH Aachen University, Aachen, Germany
| | - Andreas Keller
- Clinical Bioinformatics, Saarland University, Saarbrücken, Germany.,Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA, USA
| | - Robert Bals
- Department of Internal Medicine V, Saarland University Hospital, Homburg, Germany
| | - Peter Nürnberg
- Cologne Center for Genomics, West German Genome Center, University of Cologne, Cologne, Germany
| | - Olaf Rieß
- Institute of Medical Genetics and Applied Genomics, University of Tübingen, Tübingen, Germany.,NGS Competence Center Tübingen, Tübingen, Germany
| | - Philip Rosenstiel
- Institute of Clinical Molecular Biology, Christian-Albrechts-University and University Hospital Schleswig-Holstein, Kiel, Germany
| | - Mihai G Netea
- Department of Internal Medicine and Radboud Center for Infectious Diseases (RCI), Radboud University Medical Center, Nijmegen, The Netherlands.,Immunology & Metabolism, Life and Medical Sciences (LIMES) Institute, University of Bonn, Bonn, Germany
| | - Fabian Theis
- Institute of Computational Biology, Helmholtz Center Munich (HMGU), Neuherberg, Germany
| | - Sach Mukherjee
- Statistics and Machine Learning, Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE), Bonn, Germany
| | - Michael Backes
- CISPA Helmholtz Center for Information Security, Saarbrücken, Germany
| | - Anna C Aschenbrenner
- Systems Medicine, Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE), Bonn, Germany.,Genomics and Immunoregulation, Life & Medical Sciences (LIMES) Institute, University of Bonn, Bonn, Germany.,PRECISE Platform for Single Cell Genomics and Epigenomics, Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE) and the University of Bonn, Bonn, Germany.,Department of Internal Medicine and Radboud Center for Infectious Diseases (RCI), Radboud University Medical Center, Nijmegen, The Netherlands
| | - Thomas Ulas
- Systems Medicine, Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE), Bonn, Germany.,Genomics and Immunoregulation, Life & Medical Sciences (LIMES) Institute, University of Bonn, Bonn, Germany
| | | | - Monique M B Breteler
- Population Health Sciences, Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE), Bonn, Germany.,Institute for Medical Biometry, Informatics and Epidemiology (IMBIE), Faculty of Medicine, University of Bonn, Bonn, Germany
| | | | - Matthijs Kox
- Department of Intensive Care Medicine and Radboud Center for Infectious Diseases (RCI), Radboud University Medical Center, Nijmegen, The Netherlands
| | - Matthias Becker
- Systems Medicine, Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE), Bonn, Germany.,PRECISE Platform for Single Cell Genomics and Epigenomics, Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE) and the University of Bonn, Bonn, Germany
| | | | | | - Eng Lim Goh
- Hewlett Packard Enterprise, Houston, TX, USA
| | - Joachim L Schultze
- Systems Medicine, Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE), Bonn, Germany. .,Genomics and Immunoregulation, Life & Medical Sciences (LIMES) Institute, University of Bonn, Bonn, Germany. .,PRECISE Platform for Single Cell Genomics and Epigenomics, Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE) and the University of Bonn, Bonn, Germany.
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Velmovitsky PE, Bevilacqua T, Alencar P, Cowan D, Morita PP. Convergence of Precision Medicine and Public Health Into Precision Public Health: Toward a Big Data Perspective. Front Public Health 2021; 9:561873. [PMID: 33889555 PMCID: PMC8055845 DOI: 10.3389/fpubh.2021.561873] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2020] [Accepted: 03/10/2021] [Indexed: 12/23/2022] Open
Abstract
The field of precision medicine explores disease treatments by looking at genetic, socio-environmental, and clinical factors, thus trying to provide a holistic view of a person's health. Public health, on the other hand, is focused on improving the health of populations through preventive strategies and timely interventions. With recent advances in technology, we are able to collect, analyze and store for the first-time large volumes of real-time, diverse and continuous health data. Typically, the field of precision medicine deals with a huge amount of data from few individuals; public health, on the other hand, deals with limited data from a population. With the coming of Big Data, the fields of precision medicine and public health are converging into precision public health, the study of biological and genetic factors supported by large amounts of population data. In this paper, we explore through a comprehensive review the data types and use cases found in precision medicine and public health. We also discuss how these data types and use cases can converge toward precision public health, as well as challenges and opportunities provided by research and analyses of health data.
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Affiliation(s)
| | - Tatiana Bevilacqua
- School of Public Health and Health Systems, University of Waterloo, Waterloo, ON, Canada
| | - Paulo Alencar
- David R. Cheriton School of Computer Science, University of Waterloo, Waterloo, ON, Canada.,Waterloo Artificial Intelligence Institute (Waterloo.ai), Waterloo, ON, Canada
| | - Donald Cowan
- David R. Cheriton School of Computer Science, University of Waterloo, Waterloo, ON, Canada.,Waterloo Artificial Intelligence Institute (Waterloo.ai), Waterloo, ON, Canada
| | - Plinio Pelegrini Morita
- School of Public Health and Health Systems, University of Waterloo, Waterloo, ON, Canada.,Institute of Health Policy, Management, and Evaluation, Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada.,eHealth Innovation, Techna Institute, University Health Network, Toronto, ON, Canada
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Bardsley EN, Paterson DJ. Neurocardiac regulation: from cardiac mechanisms to novel therapeutic approaches. J Physiol 2020; 598:2957-2976. [PMID: 30307615 PMCID: PMC7496613 DOI: 10.1113/jp276962] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2018] [Accepted: 10/02/2018] [Indexed: 12/15/2022] Open
Abstract
Cardiac sympathetic overactivity is a well-established contributor to the progression of neurogenic hypertension and heart failure, yet the underlying pathophysiology remains unclear. Recent studies have highlighted the importance of acutely regulated cyclic nucleotides and their effectors in the control of intracellular calcium and exocytosis. Emerging evidence now suggests that a significant component of sympathetic overactivity and enhanced transmission may arise from impaired cyclic nucleotide signalling, resulting from compromised phosphodiesterase activity, as well as alterations in receptor-coupled G-protein activation. In this review, we address some of the key cellular and molecular pathways that contribute to sympathetic overactivity in hypertension and discuss their potential for therapeutic targeting.
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Affiliation(s)
- E. N. Bardsley
- Wellcome Trust OXION Initiative in Ion Channels and DiseaseOxfordUK
- Burdon Sanderson Cardiac Science Centre, Department of PhysiologyAnatomy and Genetics, University of OxfordOxfordOX1 3PTUK
| | - D. J. Paterson
- Wellcome Trust OXION Initiative in Ion Channels and DiseaseOxfordUK
- Burdon Sanderson Cardiac Science Centre, Department of PhysiologyAnatomy and Genetics, University of OxfordOxfordOX1 3PTUK
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8
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Sun Y, Zhao L, Lan Z, Jia XZ, Xue SW. Differentiating Boys with ADHD from Those with Typical Development Based on Whole-Brain Functional Connections Using a Machine Learning Approach. Neuropsychiatr Dis Treat 2020; 16:691-702. [PMID: 32210565 PMCID: PMC7071874 DOI: 10.2147/ndt.s239013] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/18/2019] [Accepted: 03/01/2020] [Indexed: 12/20/2022] Open
Abstract
PURPOSE In recent years, machine learning techniques have received increasing attention as a promising approach to differentiating patients from healthy subjects. Therefore, some resting-state functional magnetic resonance neuroimaging (R-fMRI) studies have used interregional functional connections as discriminative features. The aim of this study was to investigate ADHD-related spatially distributed discriminative features derived from whole-brain resting-state functional connectivity patterns using machine learning. PATIENTS AND METHODS We measured the interregional functional connections of the R-fMRI data from 40 ADHD patients and 28 matched typically developing controls. Machine learning was used to discriminate ADHD patients from controls. Classification performance was assessed by permutation tests. RESULTS The results from the model with the highest classification accuracy showed that 85.3% of participants were correctly identified using leave-one-out cross-validation (LOOV) with support vector machine (SVM). The majority of the most discriminative functional connections were located within or between the cerebellum, default mode network (DMN) and frontoparietal regions. Approximately half of the most discriminative connections were associated with the cerebellum. The cerebellum, right superior orbitofrontal cortex, left olfactory cortex, left gyrus rectus, right superior temporal pole, right calcarine gyrus and bilateral inferior occipital cortex showed the highest discriminative power in classification. Regarding the brain-behaviour relationships, some functional connections between the cerebellum and DMN regions were significantly correlated with behavioural symptoms in ADHD (P < 0.05). CONCLUSION This study indicated that whole-brain resting-state functional connections might provide potential neuroimaging-based information for clinically assisting the diagnosis of ADHD.
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Affiliation(s)
- Yunkai Sun
- Center for Cognition and Brain Disorders, Institute of Psychological Sciences and the Affiliated Hospital, Hangzhou Normal University, Hangzhou 311121, People's Republic of China.,Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Hangzhou 311121, People's Republic of China
| | - Lei Zhao
- Center for Cognition and Brain Disorders, Institute of Psychological Sciences and the Affiliated Hospital, Hangzhou Normal University, Hangzhou 311121, People's Republic of China.,Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Hangzhou 311121, People's Republic of China
| | - Zhihui Lan
- Center for Cognition and Brain Disorders, Institute of Psychological Sciences and the Affiliated Hospital, Hangzhou Normal University, Hangzhou 311121, People's Republic of China.,Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Hangzhou 311121, People's Republic of China
| | - Xi-Ze Jia
- Center for Cognition and Brain Disorders, Institute of Psychological Sciences and the Affiliated Hospital, Hangzhou Normal University, Hangzhou 311121, People's Republic of China.,Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Hangzhou 311121, People's Republic of China
| | - Shao-Wei Xue
- Center for Cognition and Brain Disorders, Institute of Psychological Sciences and the Affiliated Hospital, Hangzhou Normal University, Hangzhou 311121, People's Republic of China.,Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Hangzhou 311121, People's Republic of China
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9
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Passos IC, Ballester PL, Barros RC, Librenza-Garcia D, Mwangi B, Birmaher B, Brietzke E, Hajek T, Lopez Jaramillo C, Mansur RB, Alda M, Haarman BCM, Isometsa E, Lam RW, McIntyre RS, Minuzzi L, Kessing LV, Yatham LN, Duffy A, Kapczinski F. Machine learning and big data analytics in bipolar disorder: A position paper from the International Society for Bipolar Disorders Big Data Task Force. Bipolar Disord 2019; 21:582-594. [PMID: 31465619 DOI: 10.1111/bdi.12828] [Citation(s) in RCA: 39] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
OBJECTIVES The International Society for Bipolar Disorders Big Data Task Force assembled leading researchers in the field of bipolar disorder (BD), machine learning, and big data with extensive experience to evaluate the rationale of machine learning and big data analytics strategies for BD. METHOD A task force was convened to examine and integrate findings from the scientific literature related to machine learning and big data based studies to clarify terminology and to describe challenges and potential applications in the field of BD. We also systematically searched PubMed, Embase, and Web of Science for articles published up to January 2019 that used machine learning in BD. RESULTS The results suggested that big data analytics has the potential to provide risk calculators to aid in treatment decisions and predict clinical prognosis, including suicidality, for individual patients. This approach can advance diagnosis by enabling discovery of more relevant data-driven phenotypes, as well as by predicting transition to the disorder in high-risk unaffected subjects. We also discuss the most frequent challenges that big data analytics applications can face, such as heterogeneity, lack of external validation and replication of some studies, cost and non-stationary distribution of the data, and lack of appropriate funding. CONCLUSION Machine learning-based studies, including atheoretical data-driven big data approaches, provide an opportunity to more accurately detect those who are at risk, parse-relevant phenotypes as well as inform treatment selection and prognosis. However, several methodological challenges need to be addressed in order to translate research findings to clinical settings.
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Affiliation(s)
- Ives C Passos
- Laboratory of Molecular Psychiatry and Bipolar Disorder Program, Programa de Pós-Graduação em Psiquiatria e Ciências do Comportamento, Hospital de Clínicas de Porto Alegre, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil
| | - Pedro L Ballester
- School of Technology, Pontifícia Universidade Católica do Rio Grande do Sul, Rio Grande do Sul, Brazil
| | - Rodrigo C Barros
- School of Technology, Pontifícia Universidade Católica do Rio Grande do Sul, Rio Grande do Sul, Brazil
| | - Diego Librenza-Garcia
- Department of Psychiatry and Behavioural Neurosciences, McMaster University, Hamilton, ON, Canada
| | - Benson Mwangi
- Department of Psychiatry and Behavioral Sciences, UT Center of Excellence on Mood Disorders, McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Boris Birmaher
- Department of Psychiatry, Western Psychiatric Institute and Clinic, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Elisa Brietzke
- Department of Psychiatry, Queen's University School of Medicine, Kingston, ON, Canada
| | - Tomas Hajek
- Department of Psychiatry, Dalhousie University, Halifax, NS, Canada.,National Institute of Mental Health, Klecany, Czech Republic
| | - Carlos Lopez Jaramillo
- Research Group in Psychiatry, Department of Psychiatry, Faculty of Medicine, University of Antioquia, Medellín, Colombia.,Mood Disorders Program, Hospital Universitario San Vicente Fundación, Medellín, Colombia
| | - Rodrigo B Mansur
- Mood Disorders Psychopharmacology Unit (MDPU), University Health Network, University of Toronto, Toronto, ON, Canada
| | - Martin Alda
- Department of Psychiatry, Dalhousie University, Halifax, NS, Canada
| | - Bartholomeus C M Haarman
- Department of Psychiatry, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Erkki Isometsa
- Department of Psychiatry, University of Helsinki and Helsinki University Central Hospital, Helsinki, Finland
| | - Raymond W Lam
- Department of Psychiatry, University of British Columbia, Vancouver, BC, Canada
| | - Roger S McIntyre
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada
| | - Luciano Minuzzi
- Department of Psychiatry and Behavioural Neurosciences, McMaster University, Hamilton, ON, Canada
| | - Lars V Kessing
- Copenhagen Affective Disorder Research Center (CADIC), Psychiatric Center Copenhagen, Copenhagen University Hospital, Copenhagen, Denmark
| | - Lakshmi N Yatham
- Department of Psychiatry, University of British Columbia, Vancouver, BC, Canada
| | - Anne Duffy
- Department of Psychiatry, Queen's University School of Medicine, Kingston, ON, Canada
| | - Flavio Kapczinski
- Department of Psychiatry and Behavioural Neurosciences, McMaster University, Hamilton, ON, Canada
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10
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Qiu Q, Nian YJ, Guo Y, Tang L, Lu N, Wen LZ, Wang B, Chen DF, Liu KJ. Development and validation of three machine-learning models for predicting multiple organ failure in moderately severe and severe acute pancreatitis. BMC Gastroenterol 2019; 19:118. [PMID: 31272385 PMCID: PMC6611034 DOI: 10.1186/s12876-019-1016-y] [Citation(s) in RCA: 33] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/22/2018] [Accepted: 06/07/2019] [Indexed: 12/11/2022] Open
Abstract
BACKGROUND Multiple organ failure (MOF) is a serious complication of moderately severe (MASP) and severe acute pancreatitis (SAP). This study aimed to develop and assess three machine-learning models to predict MOF. METHODS Patients with MSAP and SAP who were admitted from July 2014 to June 2017 were included. Firstly, parameters with significant differences between patients with MOF and without MOF were screened out by univariate analysis. Then, support vector machine (SVM), logistic regression analysis (LRA) and artificial neural networks (ANN) models were constructed based on these factors, and five-fold cross-validation was used to train each model. RESULTS A total of 263 patients were enrolled. Univariate analysis screened out sixteen parameters referring to blood volume, inflammatory, coagulation and renal function to construct machine-learning models. The predictive efficiency of the optimal combinations of features by SVM, LRA, and ANN was almost equal (AUC = 0.840, 0.832, and 0.834, respectively), as well as the Acute Physiology and Chronic Health Evaluation II score (AUC = 0.814, P > 0.05). The common important predictive factors were HCT, K-time, IL-6 and creatinine in three models. CONCLUSIONS Three machine-learning models can be efficient prognostic tools for predicting MOF in MSAP and SAP. ANN is recommended, which only needs four common parameters.
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Affiliation(s)
- Qiu Qiu
- Department of Gastroenterology, Daping Hospital, Army Medical University (Third Military Medical University), Chongqing, 400042, China.,Department of Gastroenterology, People's Hospital of Chongqing Hechuan, Chongqing, 401520, China
| | - Yong-Jian Nian
- Department of Medical Images, College of Biomedical Engineering and Imaging Medicine, Army Medical University (Third Military Medical University), Chongqing, 400038, China
| | - Yan Guo
- Department of Gastroenterology, Daping Hospital, Army Medical University (Third Military Medical University), Chongqing, 400042, China
| | - Liang Tang
- Department of Gastroenterology, Daping Hospital, Army Medical University (Third Military Medical University), Chongqing, 400042, China
| | - Nan Lu
- Department of Medical Images, College of Biomedical Engineering and Imaging Medicine, Army Medical University (Third Military Medical University), Chongqing, 400038, China
| | - Liang-Zhi Wen
- Department of Gastroenterology, Daping Hospital, Army Medical University (Third Military Medical University), Chongqing, 400042, China
| | - Bin Wang
- Department of Gastroenterology, Daping Hospital, Army Medical University (Third Military Medical University), Chongqing, 400042, China
| | - Dong-Feng Chen
- Department of Gastroenterology, Daping Hospital, Army Medical University (Third Military Medical University), Chongqing, 400042, China.
| | - Kai-Jun Liu
- Department of Gastroenterology, Daping Hospital, Army Medical University (Third Military Medical University), Chongqing, 400042, China.
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Abstract
The development of high-throughput, data-intensive biomedical research assays and technologies has created a need for researchers to develop strategies for analyzing, integrating, and interpreting the massive amounts of data they generate. Although a wide variety of statistical methods have been designed to accommodate 'big data,' experiences with the use of artificial intelligence (AI) techniques suggest that they might be particularly appropriate. In addition, the results of the application of these assays reveal a great heterogeneity in the pathophysiologic factors and processes that contribute to disease, suggesting that there is a need to tailor, or 'personalize,' medicines to the nuanced and often unique features possessed by individual patients. Given how important data-intensive assays are to revealing appropriate intervention targets and strategies for treating an individual with a disease, AI can play an important role in the development of personalized medicines. We describe many areas where AI can play such a role and argue that AI's ability to advance personalized medicine will depend critically on not only the refinement of relevant assays, but also on ways of storing, aggregating, accessing, and ultimately integrating, the data they produce. We also point out the limitations of many AI techniques in developing personalized medicines as well as consider areas for further research.
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Affiliation(s)
- Nicholas J Schork
- Department of Quantitative Medicine, The Translational Genomics Research Institute (TGen), Phoenix, AZ, USA.
- The City of Hope/TGen IMPACT Center, Duarte, CA, USA.
- The University of California San Diego, La Jolla, CA, USA.
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12
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Sauzéat L, Bernard E, Perret-Liaudet A, Quadrio I, Vighetto A, Krolak-Salmon P, Broussolle E, Leblanc P, Balter V. Isotopic Evidence for Disrupted Copper Metabolism in Amyotrophic Lateral Sclerosis. iScience 2018; 6:264-271. [PMID: 30240616 PMCID: PMC6137708 DOI: 10.1016/j.isci.2018.07.023] [Citation(s) in RCA: 32] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2018] [Revised: 07/08/2018] [Accepted: 07/26/2018] [Indexed: 12/12/2022] Open
Abstract
Redox-active metals are thought to be implicated in neurodegenerative diseases including amyotrophic lateral sclerosis (ALS). To address this point, we measured the concentrations of 12 elements and, for the first time, the stable isotope compositions of copper (redox-active) and zinc (redox-inactive) in human cerebrospinal fluids of 31 patients with ALS, 11 age-matched controls (CTRL), and 14 patients with Alzheimer disease. We first show that metal concentrations weakly discriminate patients with ALS from the two other groups. We then report that zinc isotopic compositions are similar in the three groups, but that patients with ALS have significantly 65copper-enriched isotopic compositions relative to CTRL and patients with AD. This result unambiguously demonstrates that copper is implicated in ALS. We suggest that this copper isotopic signature may result from abnormal protein aggregation in the brain parenchyma, and propose that isotopic analysis is a potential tool that may help unraveling the molecular mechanisms at work in ALS. Redox-active metals are implicated in ALS through oxidative stress Concentrations of these metals in CSFs of patients with ALS are non-specific Copper stable isotope composition in CSFs of patients with ALS are specific Isotopic balance between CSFs and brain is probably the mechanism
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Affiliation(s)
- Lucie Sauzéat
- Université de Lyon, ENS de Lyon, CNRS, LGL-TPE, 69007 Lyon, France
| | - Emilien Bernard
- Hospices Civils de Lyon, Hôpital Neurologique Pierre Wertheimer, Centre de Ressources et de Compétence SLA de Lyon, Service de Neurologie C, Bron, France
| | - Armand Perret-Liaudet
- Université de Lyon, CNRS UMR5292, INSERM U1028, BioRan, Lyon, France; Hospices Civils de Lyon, Neurobiology Laboratory, Biochemistry and Molecular Biology Department, Lyon, France
| | - Isabelle Quadrio
- Université de Lyon, CNRS UMR5292, INSERM U1028, BioRan, Lyon, France; Hospices Civils de Lyon, Neurobiology Laboratory, Biochemistry and Molecular Biology Department, Lyon, France
| | - Alain Vighetto
- Service Neurocognition et Neuroophtalmologie, Hôpital Neurologique, 59 Boulevard Pinel, 69677 Bron Cedex, France; Centre Mémoire Ressources Recherche de Lyon, Hospices Civils de Lyon, Hôpital des Charpennes, Villeurbanne, France; Université Lyon 1, Hospices Civils de Lyon, Centre de Recherche en Neurosciences de Lyon, équipe IMPACT, Lyon, France
| | - Pierre Krolak-Salmon
- Centre Mémoire Ressources Recherche de Lyon, Hospices Civils de Lyon, Hôpital des Charpennes, Villeurbanne, France
| | - Emmanuel Broussolle
- Université de Lyon, Faculté de Médecine Lyon Sud Charles Mérieux, Institut des Sciences Cognitives Marc Jeannerod, CNRS, UMR 5229, Lyon, France
| | - Pascal Leblanc
- Institut NeuroMyoGène, CNRS UMR5310, INSERM U1217, Faculté de Médecine Rockefeller, Université Claude Bernard Lyon I, 8 Avenue Rockefeller, 69373 Lyon Cedex 08, France
| | - Vincent Balter
- Université de Lyon, ENS de Lyon, CNRS, LGL-TPE, 69007 Lyon, France.
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13
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Sosso GC, Deringer VL, Elliott SR, Csányi G. Understanding the thermal properties of amorphous solids using machine-learning-based interatomic potentials. MOLECULAR SIMULATION 2018. [DOI: 10.1080/08927022.2018.1447107] [Citation(s) in RCA: 40] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Affiliation(s)
- Gabriele C. Sosso
- Department of Chemistry and Centre for Scientific Computing, University of Warwick , Coventry, UK
| | - Volker L. Deringer
- Department of Engineering, University of Cambridge , Cambridge, UK
- Department of Chemistry, University of Cambridge , Cambridge, UK
| | | | - Gábor Csányi
- Department of Engineering, University of Cambridge , Cambridge, UK
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