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Publisher Correction: The ENCODE Imputation Challenge: a critical assessment of methods for cross-cell type imputation of epigenomic profiles

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Genome biology 2025年第1期26卷 31页

作者： Jacob Matthew Schreiber Carles A Boix Jin Wook Lee Hongyang Li Yuanfang Guan Chun-Chieh Chang Jen-Chien Chang Alex Hawkins-Hooker Bernhard Schölkopf Gabriele Schweikert Mateo Rojas Carulla Arif Canakoglu Francesco Guzzo Luca Nanni Marco Masseroli Mark James Carman Pietro Pinoli Chenyang Hong Kevin Y Yip Jefrey P Spence Sanjit Singh Batra Yun S Song Shaun Mahony Zheng Zhang Wuwei Tan Yang Shen Yuanfei Sun Minyi Shi Jessika Adrian Richard S Sandstrom Nina P Farrell Jessica M Halow Kristen Lee Lixia Jiang Xinqiong Yang Charles B Epstein J Seth Strattan Bradley E Bernstein Michael P Snyder Manolis Kellis William S Noble Anshul Bharat Kundaje Department of Genetics Stanford University Stanford CA USA. jmschreiber91@***. Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology Cambridge MA USA. Department of Genetics Stanford University Stanford CA USA. Department of Computational Medicine and Bioinformatics University of Michigan Ann Arbor MI USA. Department of Research and Development DeepSeq.AI San Francisco CA USA. RIKEN Center for Integrative Medical Sciences Yokohama Japan. Department of Empirical Inference Max Planck Institute for Intelligent Systems Stuttgart Germany. School of Life Sciences University of Dundee Dundee UK. Department of Electronics Information and Bioengineering Politecnico Di Milano Milan Italy. Department of Computational Biology University of Lausanne Lausanne Switzerland. Department of Computer Science and Engineering The Chinese University of Hong Kong Sha Tin Hong Kong. Sanford Burnham Prebys Medical Discovery Institute San Diego CA USA. Department of Electrical Engineering and Computer Sciences University of California Berkeley Berkeley CA USA. Department of Statistics University of California Berkeley Berkeley CA USA. Department of Biochemistry & Molecular Biology Center for Eukaryotic Gene Regulation Pennsylvania State University University Park PA USA. Department of Statistics Pennsylvania State University University Park PA USA. Department of Electrical and Computer Engineering Texas A&M University College Station TX USA. Altius Institute Seattle WA USA. Epigenomics Program The Broad Institute of MIT and Harvard Cambridge MA USA. Department of Genome Sciences University of Washington Seattle WA USA. Department of Computer Science Stanford University Stanford CA USA.

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Spatiotemporal development of expanding bacterial colonies driven by emergent mechanical constraints and nutrient gradients

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Nature communications 2025年第1期16卷 4878页

作者： Harish Kannan Hui Sun Mya Warren Tolga Çağlar Pantong Yao Brian R Taylor Kinshuk Sahu Daotong Ge Matteo Mori David Kleinfeld JiaJia Dong Bo Li Terence Hwa Department of Mathematics University of California San Diego La Jolla CA USA. Department of Mathematics and Statistics California State University Long Beach CA USA. paul.sun@csulb.edu. Department of Physics University of California San Diego La Jolla CA USA. San Diego Supercomputer Center University of California San Diego La Jolla CA USA. Neurosciences Graduate Program University of California San Diego La Jolla CA USA. Joint BioEnergy Institute Emeryville CA USA and Systems Biology Department Sandia National Laboratories Livermore CA USA. Department of Bioengineering University of California San Diego La Jolla CA USA. Department of Biomedical Engineering Duke University Durham NC USA. Department of Neurobiology University of California San Diego La Jolla CA USA. Department of Physics & Astronomy Bucknell University Lewisburg PA USA. jiajia.dong@bucknell.edu. Department of Mathematics and Ph.D. Program in Quantitative Biology University of California San Diego La Jolla CA USA. bli@ucsd.edu. Department of Physics University of California San Diego La Jolla CA USA. hwa@ucsd.edu.

Bacterial colonies growing on solid surfaces can exhibit robust expansion kinetics, with constant radial growth and saturating vertical expansion, suggesting a common developmental program. Here, we study this process for Escherichia coli cells using a combination of modeling and experiments. We show that linear radial colony expansion is set by the verticalization of interior cells due to mechanical constraints rather than radial nutrient gradients as commonly assumed. In contrast, vertical expansion slows down from an initial linear regime even while radial expansion continues linearly. This vertical slowdown is due to limitation of cell growth caused by vertical nutrient gradients, exacerbated by concurrent oxygen depletion. Starvation in the colony interior results in a distinct death zone which sets in as vertical expansion slows down, with the death zone increasing in size along with the expanding colony. Thus, our study reveals complex heterogeneity within simple monoclonal bacterial colonies, especially along the vertical dimension. The intricate dynamics of such emergent behavior can be understood quantitatively from an interplay of mechanical constraints and nutrient gradients arising from obligatory metabolic processes.

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A Stochastic MPEC Approach for Grid Tariff Design with Demand Side Flexibility

TechRxiv

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TechRxiv 2019年

作者： Askeland, Magnus Burandt, Thorsten Gabriel, Steven A. SINTEF Energy Research Department of Electric Power Engineering Norwegian University of Science and Technology Trondheim Norway Technical University of Berlin The Energy Transportation Environment Department German Institute for Economic Research DIW Berlin Berlin Germany Department of Industrial Economics and Technology Management Norwegian University of Science and Technology Trondheim Norway Department of Mechanical Engineering Applied Math & Statistics Scientific Computation Program University of Maryland College Park Maryland United States

As the end-users increasingly can provide flexibility to the power system, it is important to consider how this flexibility can be activated as a resource for the grid. Electricity network tariffs are one option that can be used to activate this flexibility. Therefore, by designing efficient grid tariffs, it might be possible to reduce the total costs in the power system by incentivizing a change in consumption patterns. This paper provides a methodology for optimal grid tariff design under decentralized decision-making and uncertainty in demand, power prices, and renewable generation. A bilevel model is formulated to adequately describe the interaction between the end-users and a distribution system operator. In addition, a centralized decision-making model is provided for benchmarking purposes. The bilevel model is reformulated as a mixed-integer linear problem solvable by branch-and-cut techniques. Results for a deterministic example and a stochastic case study are presented and discussed. © 2019, CC BY-SA.

关键词： Decision making

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FUTURE-AI: International consensus guideline for trustworthy and deployable artificial intelligence in healthcare

arXiv

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arXiv 2023年

作者： Lekadir, Karim Feragen, Aasa Fofanah, Abdul Joseph Frangi, Alejandro F. Buyx, Alena Emelie, Anais Lara, Andrea Porras, Antonio R. Chan, An-Wen Navarro, Arcadi Glocker, Ben Botwe, Benard O. Khanal, Bishesh Beger, Brigit Wu, Carol C. Cintas, Celia Langlotz, Curtis P. Rueckert, Daniel Mzurikwao, Deogratias Fotiadis, Dimitrios I. Zhussupov, Doszhan Ferrante, Enzo Meijering, Erik Weicken, Eva González, Fabio A. Asselbergs, Folkert W. Prior, Fred Krestin, Gabriel P. Collins, Gary S. Tegenaw, Geletaw S. Kaissis, Georgios Misuraca, Gianluca Tsakou, Gianna Dwivedi, Girish Kondylakis, Haridimos Jayakody, Harsha Woodruf, Henry C. Mayer, Horst Joachim Aerts, Hugo JWL Walsh, Ian Chouvarda, Ioanna Buvat, Irène Tributsch, Isabell Rekik, Islem Duncan, James Kalpathy-Cramer, Jayashree Zahir, Jihad Park, Jinah Mongan, John Gichoya, Judy W. Schnabel, Julia A. Kushibar, Kaisar Riklund, Katrine Mori, Kensaku Marias, Kostas Amugongo, Lameck M. Fromont, Lauren A. Maier-Hein, Lena Alberich, Leonor Cerdá Rittner, Leticia Phiri, Lighton Marrakchi-Kacem, Linda Donoso-Bach, Lluís Martí-Bonmatí, Luis Cardoso, M. Jorge Bobowicz, Maciej Shabani, Mahsa Tsiknakis, Manolis Zuluaga, Maria A. Bielikova, Maria Fritzsche, Marie-Christine Camacho, Marina Linguraru, Marius George Wenzel, Markus De Bruijne, Marleen Tolsgaard, Martin G. Ghassemi, Marzyeh Ashrafuzzaman, Md Goisauf, Melanie Yaqub, Mohammad Abadía, Mónica Cano Mahmoud, Mukhtar M.E. Elattar, Mustafa Rieke, Nicola Papanikolaou, Nikolaos Lazrak, Noussair Díaz, Oliver Salvado, Olivier Pujol, Oriol Sall, Ousmane Guevara, Pamela Gordebeke, Peter Lambin, Philippe Brown, Pieta Abolmaesumi, Purang Dou, Qi Lu, Qinghua Osuala, Richard Nakasi, Rose Zhou, S. Kevin Napel, Sandy Colantonio, Sara Albarqouni, Shadi Joshi, Smriti Carter, Stacy Klein, Stefan Petersen, Steffen E. Aussó, Susanna Awate, Suyash Raviv, Tammy Riklin Cook, Tessa Mutsvangwa, Tinashe E.M. Rogers, Wendy A. Niessen, Wiro J. Puig-Bosch, Xènia Zeng, Yi Mohammed, Yunusa G. Aquino, Yves Saint James Salahuddin, Zohaib Starmans, Martijn P.A. Department de Matemàtiques i Informàtica Universitat de Barcelona Barcelona Spain Barcelona Spain DTU Compute Technical University of Denmark Kgs Lyngby Denmark Department of Mathematics and Computer Science Faculty of Science and Technology Milton Margai Technical University Freetown Sierra Leone Center for Computational Imaging & Simulation Technologies in Biomedicine Schools of Computing and Medicine University of Leeds Leeds United Kingdom Cardiovascular Science and Electronic Engineering Departments KU Leuven Leuven Belgium Institute of History and Ethics in Medicine Technical University of Munich Munich Germany Faculty of Engineering of Systems Informatics and Sciences of Computing Galileo University Guatemala City Guatemala Department of Biostatistics and Informatics Colorado School of Public Health University of Colorado Anschutz Medical Campus AuroraCO United States Department of Medicine Women’s College Research Institute University of Toronto Toronto Canada Universitat Pompeu Fabra BarcelonaBeta Brain Research Center Barcelona Spain Department of Computing Imperial College London London United Kingdom School of Biomedical & Allied Health Sciences University of Ghana Accra Ghana Department of Midwifery & Radiography School of Health & Psychological Sciences City University of London United Kingdom Kathmandu Nepal European Heart Network Brussels Belgium Department of Thoracic Imaging University of Texas MD Anderson Cancer Center Houston United States IBM Research Africa Nairobi Kenya Departments of Radiology Medicine and Biomedical Data Science Stanford University School of Medicine Stanford United States Institute for AI and Informatics in Medicine Klinikum rechts der Isar Technical University Munich Munich Germany Department of Computing Imperial College London London United Kingdom Muhimbili University of Health and Allied Sciences Dar es Salaam Tanzania United Republic of Ioannina Greece Almaty AI Lab Almaty Kazakhstan

Background: Despite major advances in artificial intelligence (AI) research for healthcare, the deployment and adoption of AI technologies remain limited in clinical practice. In recent years, concerns have been raised about the clinical, technical, ethical and legal risks associated with healthcare AI. To increase adoption in the real world, it is essential that AI technologies are trusted and accepted by patients, clinicians, health organisations and authorities. This paper describes the FUTURE-AI framework as the first international consensus guideline for trustworthy AI in healthcare. Methods: The FUTURE-AI consortium was founded in 2021 and now comprises 117 interdisciplinary experts from 50 countries representing all continents, including AI scientists, clinical researchers, biomedical ethicists, and social scientists. Over a two-year period, the consortium established guiding principles and best practices for trustworthy and deployable AI through an iterative process comprising an in-depth literature review, a modified Delphi survey, and online consensus meetings. Findings: The FUTURE-AI framework was established based on six guiding principles for trustworthy AI in healthcare, i.e. Fairness, Universality, Traceability, Usability, Robustness and Explainability. Through consensus, a set of 30 best practices were defined, addressing technical, clinical, socio-ethical and legal dimensions of trustworthy AI. The recommendations cover the entire lifecycle of healthcare AI, from design, development and validation to regulation, deployment, and monitoring. Interpretation: FUTURE-AI is a structured, risk-informed framework which provides guidance for constructing healthcare AI tools that will be trusted, deployed and adopted in real-world clinical practice. Researchers are encouraged to take the recommendations into account in proof-of-concept stages to facilitate future translation towards clinical practice of healthcare AI. Copyright © 2023, The Authors. All rights

关键词： Clinical research

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Influence of autapses on synchronisation in neural networks with chemical synapses

arXiv

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arXiv 2020年

作者： Protachevicz, Paulo R. Iarosz, K.C. Caldas, I.L. Antonopoulos, C.G. Batista, A.M. Kurths, J. Institute of Physics University of São Paulo São Paulo SP Brazil Faculdade de Telêmaco Borba FATEB Telêmaco Borba Paraná Brazil Graduate Program in Chemical Engineering Federal University of Technology Paraná Ponta GrossaParaná Brazil Department of Mathematical Sciences University of Essex Wivenhoe Park United Kingdom Department of Mathematics and Statistics State University of Ponta Grossa Ponta Grossa PR Brazil Department Complexity Science Potsdam Institute for Climate Impact Research Potsdam Germany Department of Physics Humboldt University Berlin Germany Centre for Analysis of Complex Systems Sechenov First Moscow State Medical University Moscow Russia

A great deal of research has been devoted on the investigation of neural dynamics in various network topologies. However, only a few studies have focused on the influence of autapses, synapses from a neuron onto itself via closed loops, on neural synchronisation. Here, we build a random network with adaptive exponential integrate-and-fire neurons coupled with chemical synapses, equipped with autapses, to study the effect of the latter on synchronous behaviour. We consider time delay in the conductance of the pre-synaptic neuron for excitatory and inhibitory connections. Interestingly, in neural networks consisting of both excitatory and inhibitory neurons, we uncover that synchronous behaviour depends on their synapse type. Our results provide evidence on the synchronous and desynchronous activities that emerge in random neural networks with chemical, inhibitory and excitatory synapses where neurons are equipped with autapses. Copyright © 2020, The Authors. All rights reserved.

关键词： Synchronization

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Combined effects of hydrometeorological hazards and urbanisation on dengue risk in Brazil: a spatiotemporal modelling study

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The Lancet Planetary Health 2021年第4期5卷 e209-e219页

作者： Lowe, Rachel Lee, Sophie A Brady, Oliver J Bastos, Leonardo Carrasco-Escobar, Gabriel de Castro Catão, Rafael Colón-González, Felipe J Barcellos, Christovam Carvalho, Marilia Sá Blangiardo, Marta Rue, Håvard Gasparrini, Antonio Centre for Mathematical Modelling of Infectious Diseases London School of Hygiene & Tropical Medicine London United Kingdom Centre on Climate Change and Planetary Health London School of Hygiene & Tropical Medicine London United Kingdom Department of Infectious Disease Epidemiology London School of Hygiene & Tropical Medicine London United Kingdom Department of Public Health Environments and Society London School of Hygiene & Tropical Medicine London United Kingdom Centre for Statistical Modelling London School of Hygiene & Tropical Medicine London United Kingdom Scientific Computing Program Oswaldo Cruz Foundation Rio de Janeiro Brazil Health Information and Communication Institute Oswaldo Cruz Foundation Rio de Janeiro Brazil Scripps Institution of Oceanography University of California San Diego San Diego CA United States Health Innovation Laboratory Institute of Tropical Medicine Alexander von Humboldt Universidad Peruana Cayetano Heredia Lima Peru Department of Geography Federal University of Espírito Santo Vitória Brazil MRC Centre for Environment and Health Department of Epidemiology and Biostatistics Imperial College London London United Kingdom Statistics Program Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology Thuwal Saudi Arabia

Background: Temperature and rainfall patterns are known to influence seasonal patterns of dengue transmission. However, the effect of severe drought and extremely wet conditions on the timing and intensity of dengue epidemics is poorly understood. In this study, we aimed to quantify the non-linear and delayed effects of extreme hydrometeorological hazards on dengue risk by level of urbanisation in Brazil using a spatiotemporal model. Methods: We combined distributed lag non-linear models with a spatiotemporal Bayesian hierarchical model framework to determine the exposure-lag-response association between the relative risk (RR) of dengue and a drought severity index. We fit the model to monthly dengue case data for the 558 microregions of Brazil between January, 2001, and January, 2019, accounting for unobserved confounding factors, spatial autocorrelation, seasonality, and interannual variability. We assessed the variation in RR by level of urbanisation through an interaction between the drought severity index and urbanisation. We also assessed the effect of hydrometeorological hazards on dengue risk in areas with a high frequency of water supply shortages. Findings: The dataset included 12 895 293 dengue cases reported between 2001 and 2019 in Brazil. Overall, the risk of dengue increased between 0–3 months after extremely wet conditions (maximum RR at 1 month lag 1·56 [95% CI 1·41–1·73]) and 3–5 months after drought conditions (maximum RR at 4 months lag 1·43 [1·22–1·67]). Including a linear interaction between the drought severity index and level of urbanisation improved the model fit and showed the risk of dengue was higher in more rural areas than highly urbanised areas during extremely wet conditions (maximum RR 1·77 [1·32–2·37] at 0 months lag vs maximum RR 1·58 [1·39–1·81] at 2 months lag), but higher in highly urbanised areas than rural areas after extreme drought (maximum RR 1·60 [1·33–1·92] vs 1·15 [1·08–1·22], both at 4 months lag). We also found the den

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A Lagrangian Method for Reactive Transport with Solid/Aqueous Chemical Phase Interaction i

arXiv

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arXiv 2018年

作者： Schmidt, Michael J. Pankavich, Stephen D. Navarre-Sitchler, Alexis Benson, David A. Hydrologic Science and Engineering Program Department of Geology and Geological Engineering Colorado School of Mines GoldenCO80401 United States Department of Applied Mathematics and Statistics Colorado School of Mines GoldenCO80401 United States

A significant drawback of Lagrangian (particle-tracking) reactive transport models has been their inability to properly simulate interactions between solid and liquid chemical phases, such as dissolution and precipitation reactions. This work addresses that problem by implementing a mass-transfer algorithm between mobile and immobile sets of particles that allows aqueous species of reactant that are undergoing transport to interact with stationary solid species. This mass-transfer algorithm is demonstrated to solve the diffusion equation for an arbitrarily small level of diffusion and thus does not introduce any spurious mixing. The algorithm can be combined with random walks to simulate the desired total level of diffusion in a reactive transport system. Copyright © 2018, The Authors. All rights reserved.

关键词： Diffusion

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Influence of inhibitory synapses on the criticality of excitable neuronal networks

arXiv

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arXiv 2020年

作者： Borges, F.S. Protachevicz, P.R. Santos, V. Santos, M.S. Gabrick, E.C. Iarosz, K.C. Lameu, E.L. Baptista, M.S. Caldas, I.L. Batista, A.M. Center for Mathematics Computation and Cognition Federal University of ABC São Bernardo do CampoSP09606-045 Brazil Graduate Program in Science - Physics State University of Ponta Grossa Ponta Grossa PR84030-900 Brazil Institute of Physics University of São Paulo São PauloSP05508-900 Brazil Faculdade de Telêmaco Borba Telêmaco BorbaPR84266-010 Brazil Graduate Program in Chemical Engineering Federal University of Technology Paraná Ponta Grossa PR84016-210 Brazil Cell Biology and Anatomy Department University of Calgary AB T2N 4N1 CalgaryAB Canada Institute for Complex Systems and Mathematical Biology University of Aberdeen AberdeenScotlandAB24 3UE United Kingdom Department of Mathematics and Statistics State University of Ponta Grossa Ponta Grossa PR84030-900 Brazil

In this work, we study the dynamic range of a neuronal network of excitable neurons with excitatory and inhibitory synapses. We obtain an analytical expression for the critical point as a function of the excitatory and inhibitory synaptic intensities. We also determine an analytical expression that gives the critical point value in which the maximal dynamic range occurs. Depending on the mean connection degree and coupling weights, the critical points can exhibit ceasing or ceaseless dynamics. However, the dynamic range is equal in both cases. We observe that the external stimulus mask some effects of self-sustained activity (ceaseless dynamic) in the region where the dynamic range is calculated. In these regions, the firing rate is the same for ceaseless dynamics and ceasing activity. Furthermore, we verify that excitatory and inhibitory inputs are approximately equal for a network with a large number of connections, showing excitatory-inhibitory balance as reported experimentally. Copyright © 2020, The Authors. All rights reserved.

关键词： Dynamics

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Considering discrepancy when calibrating a mechanistic electrophysiology model

arXiv

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arXiv 2020年

作者： Lei, Chon Lok Ghosh, Sanmitra Whittaker, Dominic G. Aboelkassem, Yasser Beattie, Kylie A. Cantwell, Chris D. Delhaas, Tammo Houston, Charles Novaes, Gustavo Montes Panfilov, Alexander V. Pathmanathan, Pras Riabiz, Marina Weber dos Santos, Rodrigo Worden, Keith Mirams, Gary R. Wilkinson, Richard D. Computational Biology & Health Informatics Dept. of Computer Science University of Oxford United Kingdom MRC Biostatistics Unit University of Cambridge United Kingdom Centre for Mathematical Medicine & Biology School of Mathematical Sciences University of Nottingham United Kingdom Department of Bioengineering University of California San Diego United States Systems Modeling and Translational Biology GlaxoSmithKline R&D Stevenage United Kingdom ElectroCardioMaths Programme Centre for Cardiac Engineering Imperial College London United Kingdom CARIM School for Cardiovascular Diseases Maastricht University Netherlands Graduate Program in Computational Modeling Universidade Federal de Juiz de Fora Brazil Department of Physics and Astronomy Ghent University Belgium Laboratory of Computational Biology and Medicine Ural Federal University Ekaterinburg Russia U.S. Food and Drug Administration Center for Devices and Radiological Health Office of Science and Engineering Laboratories United States Department of Biomedical Engineering King’s College London Alan Turing Institute United Kingdom Dynamics Research Group Department of Mechanical Engineering University of Sheffield United Kingdom School of Mathematics and Statistics University of Sheffield United Kingdom

Uncertainty quantification (UQ) is a vital step in using mathematical models and simulations to take decisions. The field of cardiac simulation has begun to explore and adopt UQ methods to characterise uncertainty in model inputs and how that propagates through to outputs or predictions. In this perspective piece we draw attention to an important and under-addressed source of uncertainty in our predictions — that of uncertainty in the model structure or the equations themselves. The difference between imperfect models and reality is termed model discrepancy, and we are often uncertain as to the size and consequences of this discrepancy. Here we provide two examples of the consequences of discrepancy when calibrating models at the ion channel and action potential scales. Furthermore, we attempt to account for this discrepancy when calibrating and validating an ion channel model using different methods, based on modelling the discrepancy using Gaussian processes (GPs) and autoregressive-moving-average (ARMA) models, then highlight the advantages and shortcomings of each approach. Finally, suggestions and lines of enquiry for future work are provided. Copyright © 2020, The Authors. All rights reserved.

关键词： Uncertainty analysis

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Ten quick tips for deep learning in biology

arXiv

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arXiv 2021年

作者： Lee, Benjamin D. Gitter, Anthony Greene, Casey S. Raschka, Sebastian Maguire, Finlay Titus, Alexander J. Kessler, Michael D. Lee, Alexandra J. Chevrette, Marc G. Stewart, Paul Allen Britto-Borges, Thiago Cofer, Evan M. Yu, Kun-Hsing Carmona, Juan Jose Fertig, Elana J. Kalinin, Alexandr A. Signal, Beth Lengerich, Benjamin J. Triche, Timothy J. Boca, Simina M. In-Q-Tel Labs School of Engineering and Applied Sciences Harvard University Department of Genetics Harvard Medical School United States Department of Biostatistics and Medical Informatics University of Wisconsin-Madison MadisonWI United States Morgridge Institute for Research MadisonWI United States Department of Systems Pharmacology and Translational Therapeutics Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Biochemistry and Molecular Genetics University of Colorado School of Medicine AuroraCO United States Center for Health AI University of Colorado School of Medicine AuroraCO United States Department of Statistics University of Wisconsin Madison United States Faculty of Computer Science Dalhousie University Canada University of New Hampshire Bioeconomy.XYZ United States Department of Oncology Johns Hopkins University United States Institute for Genome Sciences University of Maryland School of Medicine United States Genomics and Computational Biology Graduate Program University of Pennsylvania United States Department of Systems Pharmacology and Translational Therapeutics University of Pennsylvania United States Wisconsin Institute for Discovery Department of Plant Pathology University of Wisconsin-Madison United States Department of Biostatistics and Bioinformatics Moffitt Cancer Center TampaFL United States Section of Bioinformatics and Systems Cardiology Klaus Tschira Institute for Integrative Computational Cardiology University Hospital Heidelberg Germany University Hospital Heidelberg Germany Lewis-Sigler Institute for Integrative Genomics Princeton University PrincetonNJ United States Graduate Program in Quantitative and Computational Biology Princeton University PrincetonNJ United States Department of Biomedical Informatics Harvard Medical School United States Department of Pathology Brigham and Women's Hospital United States Philips Healthcare CambridgeMA United States Philips Research

Machine learning is a modern approach to problem-solving and task automation. In particular, machine learning is concerned with the development and applications of algorithms that can recognize patterns in data and use them for predictive modeling. Artificial neural networks are a particular class of machine learning algorithms and models that evolved into what is now described as deep learning. Given the computational advances made in the last decade, deep learning can now be applied to massive data sets and in innumerable contexts. Therefore, deep learning has become its own subfield of machine learning. In the context of biological research, it has been increasingly used to derive novel insights from high-dimensional biological data. To make the biological applications of deep learning more accessible to scientists who have some experience with machine learning, we solicited input from a community of researchers with varied biological and deep learning interests. These individuals collaboratively contributed to this manuscript's writing using the GitHub version control platform and the Manubot manuscript generation toolset. The goal was to articulate a practical, accessible, and concise set of guidelines and suggestions to follow when using deep learning. In the course of our discussions, several themes became clear: the importance of understanding and applying machine learning fundamentals as a baseline for utilizing deep learning, the necessity for extensive model comparisons with careful evaluation, and the need for critical thought in interpreting results generated by deep learning, among others. © 2021, CC BY.

关键词： Learning algorithms

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