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arXiv 2022年

作者： Jivanjee, Tasneem Ibrahim, Samira Nyquist, Sarah K. James Gatter, G. Bromley, Joshua D. Jaiswal, Swati Berger, Bonnie Behar, Samuel M. Christopher Love, J. Shalek, Alex K. Institute for Medical Engineering & Science Department of Chemistry Massachusetts Institute of Technology CambridgeMA United States Koch Institute for Integrative Cancer Research Massachusetts Institute of Technology CambridgeMA United States Ragon Institute of MGH MIT and Harvard CambridgeMA United States Broad Institute of MIT and Harvard CambridgeMA United States Massachusetts Institute of Technology CambridgeMA United States Program in Computational and Systems Biology Massachusetts Institute of Technology CambridgeMA United States Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology CambridgeMA United States Microbiology Graduate Program Massachusetts Institute of Technology CambridgeMA02139 United States Department of Microbiology and Physiological Systems University of Massachusetts Medical School WorcesterMA United States Department of Mathematics Massachusetts Institute of Technology CambridgeMA United States Department of Chemical Engineering Massachusetts Institute of Technology CambridgeMA United States

Antigen-specific T cells play an essential role in immunoregulation and many diseases such as cancer. Characterizing the T cell receptor (TCR) sequences that encode T cell specificity is critical for elucidating the antigenic determinants of immunological diseases and designing therapeutic remedies. However, methods of obtaining single-cell TCR sequencing data are labor and cost intensive, typically requiring both cell sorting and full length single-cell RNA-sequencing (scRNA-seq). New high-throughput 3' cell-barcoding scRNA-seq methods can simplify and scale this process;however, they do not routinely capture TCR sequences during library preparation and sequencing. While 5' cell-barcoding scRNA-seq methods can be used to examine TCR repertoire at single-cell resolution, doing so requires specialized reagents which cannot be applied to samples previously processed using 3' cell-barcoding methods. Here, we outline a method for sequencing TCRα and TCRβ transcripts from samples already processed using 3' cell-barcoding scRNA-seq platforms, ensuring TCR recovery at a single-cell resolution. In short, a fraction of the 3' barcoded whole transcriptome amplification (WTA) product typically used to generate a massively parallel 3' scRNA-seq library is enriched for TCR transcripts using biotinylated probes, and further amplified using the same universal primer sequence from WTA. Primer extension using TCR V-region primers and targeted PCR amplification using a second universal primer results in a 3' barcoded single-cell CDR3-enriched library that can be sequenced with custom sequencing primers. Coupled with 3' scRNA-seq of the same WTA, this method enables simultaneous analysis of single-cell transcriptomes and TCR sequences which can help interpret inherent heterogeneity among antigen-specific T cells and salient disease biology. The method presented here can also be adapted readily to enrich and sequence other transcripts of interest from both 3' and 5' barcoded scRNA-seq

关键词： RNA

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Analyzing dominance move (MIP-DoM) indicator for multi- And many-objective optimization

arXiv

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arXiv 2020年

作者： do Val Lopes, Claudio Lucio Martins, Flávio Vinícius Cruzeiro Wanner, Elizabeth Fialho Deb, Kalyanmoy Postgraduate Program in Mathematical and Computational Modeling CEFET-MG Brazil Computer Department Centro Federal de Educação Tecnológica de Minas Gerais 30510-000 Brazil Computer Department Centro Federal de Educação Tecnológica de Minas Gerais 30510-000 Brazil Department of Computer Science and Engineering Michigan State University East LansingMI48824 United States

—Dominance move (DoM) is a binary quality indicator that can be used in multi-objective and many-objective optimization to compare two solution sets obtained from different algorithms. The DoM indicator can differentiate the sets for certain important features, such as convergence, spread, uniformity, and cardinality. DoM does not use any reference, and it has an intuitive and physical meaning, similar to the -indicator, and calculates the minimum total move of members of one set so that all elements in another set are to be dominated or identical to at least one member of the first set. Despite the aforementioned properties, DoM is hard to calculate, particularly in higher dimensions. There is an efficient and exact method to calculate it in a bi-objective case only. This work proposes a novel approach to calculate DoM using a mixed integer programming (MIP) approach, which can handle sets with three or more objectives and is shown to overcome the -indicator’s information loss. Experiments, in the bi-objective space, are done to verify the model’s correctness. Furthermore, other experiments, using 3, 5, 10, 15, 20, 25 and 30-objective problems are performed to show how the model behaves in higher-dimensional cases. Algorithms, such as IBEA, MOEA/D, NSGA-III, NSGA-II, and SPEA2 are used to generate the solution sets (however any other algorithms can also be used with the proposed MIP-DoM indicator). Further extensions are discussed to handle certain idiosyncrasies with some solution sets and also to improve the quality indicator and its use for other situations. Copyright © 2020, The Authors. All rights reserved.

关键词： Multiobjective optimization

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Optimization and simulation methods in addressing COVID-19 physical distancing policy: A bibliometric analysis

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AIP Conference Proceedings 2022年第1期2472卷 040010页

作者： Fazillah Mohmad Kamal Mohd Kamal Mohd Nawawi Ruzelan Khalid Antoni Wibowo Amalia Zahra School of Quantitative Sciences UUM College of Arts and Sciences Universiti Utara Malaysia 06010 UUM Sintok Kedah Malaysia Institute of Strategic Industrial Decision Modeling School of Quantitative Sciences Universiti Utara Malaysia 06010 UUM Sintok Kedah Malaysia Computer Science Department Binus Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia 11480

COVID-19 has received tremendous attention from scholars worldwide and even being labelled as a black swan event that has disrupted every aspect of human life. Within a short time span of the pandemic, a large volume of research pertaining to COVID-19 has been published in diverse research fields. This paper adopts a bibliometric analysis to systematically evaluate the research development in the application of optimization and simulation methods to address COVID-19 physical distancing policy (OSPhyD) using Bibliometrix R package. A textual query on Scopus database using the combination of four classes of keywords; covid-19, optimization, simulation, and physical distance has returned a total of 299 original research articles and reviews published in English. Appropriate visualizations were generated to describe the collaborations between different authors, countries, and institutions, whilst co-word analysis that uses text mining technique has produced a conceptual cluster via co-occurrence network map to underscore the emerging themes in the current research interest. The main findings pinpoint that: 1) OSPhyD as a scientific research field is an emerging multidisciplinary research topic that is growing progressively and steadily in the fields of medicine, engineering, social sciences, mathematics and decision science, 2) The field has attracted the attention of scholars from all over the world particularly from United States of America, United Kingdom, European countries including authors from Asia, and 3) Three dominant themes or research front emerged from the publications including COVID-19 mainstream, medical education and undergraduate.

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OpenMM 8: Molecular Dynamics Simulation with Machine Learning Potentials

arXiv

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arXiv 2023年

作者： Eastman, Peter Galvelis, Raimondas Peláez, Raúl P. Abreu, Charlles R.A. Farr, Stephen E. Gallicchio, Emilio Gorenko, Anton Henry, Michael M. Hu, Frank Huang, Jing Krämer, Andreas Michel, Julien Mitchell, Joshua A. Pande, Vijay S. Rodrigues, João P.G.L.M. Rodriguez-Guerra, Jaime Simmonett, Andrew C. Singh, Sukrit Swails, Jason Turner, Philip Wang, Yuanqing Zhang, Ivy Chodera, John D. De Fabritiis, Gianni Markland, Thomas E. Department of Chemistry Stanford University StanfordCA94305 United States Acellera Labs C Dr Trueta 183 Barcelona08005 Spain C Dr. Aiguader 88 Barcelona08003 Spain Chemical Engineering Department School of Chemistry Federal University of Rio de Janeiro Rio de Janeiro68542 Brazil Redesign Science Inc. 180 Varick St. New YorkNY10014 United States EaStCHEM School of Chemistry University of Edinburgh EH9 3FJ United Kingdom Department of Chemistry and Biochemistry Brooklyn College The City University of New York NY United States Ph.D. Program in Chemistry Ph.D. Program in Biochemistry The Graduate Center of the City University of New York New YorkNY United States Stream HPC Koningin Wilhelminaplein 1 - 40601 Amsterdam1062 HG Netherlands Computational and Systems Biology Program Sloan Kettering Institute Memorial Sloan Kettering Cancer Center New YorkNY10065 United States Key Laboratory of Structural Biology of Zhejiang Province School of Life Sciences Westlake University 18 Shilongshan Road Zhejiang Hangzhou310024 China Department of Mathematics and Computer Science Freie Universität Berlin Arnimallee 12 Berlin14195 Germany The Open Force Field Initiative Open Molecular Software Foundation DavisCA95616 United States Andreessen Horowitz 2865 Sand Hill Rd Menlo ParkCA94025 United States Department of Structural Biology Stanford University StanfordCA94305 United States Charité Universitätsmedizin Berlin In silico Toxicology and Structural Bioinformatics Virchowweg 6 Berlin10117 Germany Laboratory of Computational Biology National Heart Lung and Blood Institute National Institutes of Health BethesdaMD20892 United States Entos Inc. 9310 Athena Circle La Jolla CA92037 United States College of Engineering Virginia Polytechnic Institute State University BlacksburgVA24061 United States Simons Center for Computational Physical Chemistry Center for Data Science New York University 24 Waverly Place New YorkNY10004 United States T

Machine learning plays an important and growing role in molecular simulation. The newest version of the OpenMM molecular dynamics toolkit introduces new features to support the use of machine learning potentials. Arbitrary PyTorch models can be added to a simulation and used to compute forces and energy. A higher-level interface allows users to easily model their molecules of interest with general purpose, pretrained potential functions. A collection of optimized CUDA kernels and custom PyTorch operations greatly improves the speed of simulations. We demonstrate these features on simulations of cyclin-dependent kinase 8 (CDK8) and the green fluorescent protein (GFP) chromophore in water. Taken together, these features make it practical to use machine learning to improve the accuracy of simulations at only a modest increase in cost. Copyright © 2023, The Authors. All rights reserved.

关键词： Machine learning

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Magnetized and quadratic convection based thermal transport in ternary radiative bio-nanofluid via intelligent neural networks: Two hidden layers mechanism

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Results in Physics 2024年 65卷

作者： Ayub, Assad Darvesh, Adil Shah, Syed Zahir Hussain Junjua, Moin-ud-Din Santos, Elizaldo Domingues dos Ahmad, Shabbir Hendy, Ahmed S. Ragab, Adham E. Department of Mathematics & Statistics Hazara University Manshera Pakistan School of Mathematical Sciences Zhejiang Normal University Zhejiang Jinhua 321004 China Programa de Pós-Graduação em Modelagem Computacional/LMCE Escola de Engenharia Universidade Federal do Rio Grande – FURG RS Rio Grande 96203900 Brazil FURG Escola de Engenharia RS Rio Grande Brazil Department of Basic Sciences and Humanities Muhammad Nawaz Sharif University of Engineering and Technology Multan 60000 Pakistan Department of Computational Mathematics and Computer Science Institute of Natural Sciences and Mathematics Ural Federal University 19 Mira St. Yekaterinburg 620002 Russian Federation Department of Industrial Engineering College of Engineering King Saud University P.O. Box 800 Riyadh 11421 Saudi Arabia Department of Mathematics Ghazi University Dera Ghazi Khan 32200 Pakistan Graduate Program of Ocean Engineering School of Engineering Universidade Federal do Rio Grande Italia Avenue km 8 96201-900 Rio Grande Brazil Department of Mechanics and Mathematics Western Caspian University Baku 1001 Azerbaijan

Significance: The thermal analysis of nanofluid in a vertical cylinder (artery) in a magnetized environment holds significant implications in physiological and thermal regulation networks. This research is significant in biomedical engineering with applications like medical diagnostics and treatment strategies. Motive: This study investigates the thermal behavior of a magnetized blood-based ternary Carreau nanofluid flowing through a vertically bounded artery with quadratic convection. Velocity and heat transfer analysis is conducted within the artery, utilizing thermal radiation and quadratic convection in a magnetized setting. The base fluid consists of blood, augmented with three nanoparticles: CuO, Al₂O₃, and TiO₂. The investigation centers on examining the properties of blood nanofluid, arterial geometry, and thermal dynamics. Methodology: The physical model generates a set of partial differential equations (PDEs) and similarities mechanism is utilized to fetch its non dimensional form in terms of ordinary differential equations (ODEs). Furthermore, numerical outcomes are obtained with Matlab function bvp4c and obtained data set is trained through robust scheme Levenberg-Marquardt neural network (LMNN) procedure to predict the solution. Findings: Velocity of blood is reduced with increased values of wiessenberg number, magnetic parameter and mixed convection parameter and velocity increases for Second-order convection parameter. Temperature profile decreases with curvature parameter, mixed convection parameter and permeability parameter. © 2024

关键词： Blood fluid Intelligent neural network Magnetized environment Ternary nanoparticles Thermal scrutiny

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BPS2025 - Structural basis of potassium selectivity in kalium channelrhodopsins

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Biophysical Journal 2025年第3期124卷 64a-65a页

作者： Seiya Tajima Yoon Seok Kim YoungJu Jo Peter Y. Wang Joseph M. Paggi Charu Ramakrishnan Shunki Takaramoto Takashi Nagata Masae Konno Kota Katayama Hideki Kandori Ron O. Dror Keiichi Inoue Karl Deisseroth Hideaki Kato Research Center for Advanced Science and Technology The University of Tokyo Tokyo Japan Department of Bioengineering Stanford University Stanford CA USA Department of Computer Science Stanford University Stanford CA USA Cracking the Neural Code Program Stanford University Stanford CA USA The Institute for Solid State Physics The University of Tokyo Kashiwa Japan PRESTO Japan Science and Technology Agency Kawaguchi Japan Department of Life Science and Applied Chemistry Nagoya Institute of Technology Nagoya Japan OptoBioTechnology Research Center Nagoya Institute of Technology Nagoya Japan Institute for Computational and Mathematical Engineering Stanford University Stanford CA USA Howard Hughes Medical Institute Stanford University Stanford CA USA Graduate School of Arts and Sciences The University of Tokyo Tokyo Japan

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Systematic literature review on physical distancing policy for mass gathering events

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AIP Conference Proceedings 2022年第1期2472卷

作者： Asmahany Ramely Mohd Kamal Mohd Nawawi Ruzelan Khalid Antoni Wibowo Amalia Zahra 1School of Tourism Hospitality and Event Management Universiti Utara Malaysia 06010 UUM Sintok Kedah Malaysia 2Institute of Strategic Industrial Decision Modeling School of Quantitative Sciences Universiti Utara Malaysia 06010 UUM Sintok Kedah Malaysia 3Computer Science Department Binus Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia 11480

The explosive spread of Corona Virus Disease-19 (COVID-19) in late December 2019, requires the health authorities worldwide to enforced stricter standard operating procedures (SOP) for mass gathering events, which eventually caused postponement and cancellation. This has led to a great loss and bankruptcy for most event organizers. As an effort to offer solution to this matter, a professional touch producing “soft landing” such as “Mathematical Modelling of Physical Distancing Policy for Mass Gathering Event Organizer” was proposed. Therefore, a holistic understanding about the issues related to physical distancing in mass gathering event is required. This systematic review paper summarizes current practice of physical distancing among mass gathering event organizers. This study reviewed thirteen articles using two leading databases namely Scopus and Google Scholar. Based on thematic analysis, this review finalized four themes: 1) physical distancing method; 2) the importance of physical distancing; 3) challenges in physical distancing enforcement and 4) approach to monitor the compliance of physical distancing.

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Deciphering colorectal cancer genetics through multi-omic analysis of 100,204 cases and 154,587 controls of European and east Asian ancestries (vol 55, pg 89, 2023)

引用

NATURE GENETICS 2023年第3期55卷 519-520页

作者： Fernandez-Rozadilla, Ceres Timofeeva, Maria Chen, Zhishan Law, Philip Thomas, Minta Schmit, Stephanie Diez-Obrero, Virginia Hsu, Li Fernandez-Tajes, Juan Palles, Claire Sherwood, Kitty Briggs, Sarah Svinti, Victoria Donnelly, Kevin Farrington, Susan Blackmur, James Vaughan-Shaw, Peter Shu, Xiao-ou Long, Jirong Cai, Qiuyin Guo, Xingyi Lu, Yingchang Broderick, Peter Studd, James Huyghe, Jeroen Harrison, Tabitha Conti, David Dampier, Christopher Devall, Mathew Schumacher, Fredrick Melas, Marilena Rennert, Gad Obon-Santacana, Mireia Martin-Sanchez, Vicente Moratalla-Navarro, Ferran Oh, Jae Hwan Kim, Jeongseon Jee, Sun Ha Jung, Keum Ji Kweon, Sun-Seog Shin, Min-Ho Shin, Aesun Ahn, Yoon-Ok Kim, Dong-Hyun Oze, Isao Wen, Wanqing Matsuo, Keitaro Matsuda, Koichi Tanikawa, Chizu Ren, Zefang Gao, Yu-Tang Jia, Wei-Hua Hopper, John Jenkins, Mark Win, Aung Ko Pai, Rish Figueiredo, Jane Haile, Robert Gallinger, Steven Woods, Michael Newcomb, Polly Duggan, David Cheadle, Jeremy Kaplan, Richard Maughan, Timothy Kerr, Rachel Kerr, David Kirac, Iva Bohm, Jan Mecklin, Lukka-Pekka Jousilahti, Pekka Knekt, Paul Aaltonen, Lauri Rissanen, Harri Pukkala, Eero Eriksson, Johan Cajuso, Tatiana Hanninen, Ulrika Kondelin, Johanna Palin, Kimmo Tanskanen, Tomas Renkonen-Sinisalo, Laura Zanke, Brent Mannisto, Satu Albanes, Demetrius Weinstein, Stephanie Ruiz-Narvaez, Edward Palmer, Julie Buchanan, Daniel Platz, Elizabeth Visvanathan, Kala Ulrich, Cornelia Siegel, Erin Brezina, Stefanie Gsur, Andrea Campbell, Peter Chang-Claude, Jenny Hoffmeister, Michael Brenner, Hermann Slattery, Martha Potter, John Tsilidis, Konstantinos Schulze, Matthias Gunter, Marc Murphy, Neil Castells, Antoni Castellvi-Bel, Sergi Moreira, Leticia Arndt, Volker Shcherbina, Anna Stern, Mariana Pardamean, Bens Bishop, Timothy Giles, Graham Southey, Melissa Idos, Gregory McDonnell, Kevin Abu-Ful, Zomoroda Greenson, Joel Shulman, Katerina Lejbkowicz, Flavio Offit, Kenneth Su, Yu-Ru Steinfelder, Robert Keku, Temitope van Guelpen, Bethany Hudson, Thomas Hampel, Heather Pearlman, Edinburgh Cancer Research Centre Institute of Genomics and Cancer University of Edinburgh Edinburgh UK Genomic Medicine Group Instituto de Investigacion Sanitaria de Santiago Santiago de Compostela Spain Colon Cancer Genetics Group Medical Research Council Human Genetics Unit Institute of Genetics and Cancer University of Edinburgh Edinburgh UK Danish Institute for Advanced Study Department of Public Health University of Southern Denmark Odense Denmark Division of Epidemiology Department of Medicine Vanderbilt-Ingram Cancer Center Vanderbilt Epidemiology Center Vanderbilt University Medical Center Nashville TN USA Division of Genetics and Epidemiology Institute of Cancer Research London UK Public Health Sciences Division Fred Hutchinson Cancer Research Center Seattle WA USA Genomic Medicine Institute Cleveland Clinic Cleveland OH USA Population and Cancer Prevention Program Case Comprehensive Cancer Center Cleveland OH USA Colorectal Cancer Group ONCOBELL Program Bellvitge Biomedical Research Institute Barcelona Spain Oncology Data Analytics Program Catalan Institute of Oncology Barcelona Spain Consortium for Biomedical Research in Epidemiology and Public Health Madrid Madrid Spain Department of Clinical Sciences Faculty of Medicine University of Barcelona Barcelona Spain Department of Biostatistics School of Public Health University of Washington Seattle WA USA Institute of Cancer and Genomic Sciences College of Medical and Dental Sciences University of Birmingham Birmingham UK Department of Public Health Richard Doll Building University of Oxford Oxford UK Department of Biomedical Informatics Vanderbilt University School of Medicine Nashville TN USA Department of Preventive Medicine USC Norris Comprehensive Cancer Center Keck School of Medicine University of Southern California Los Angeles CA USA Center for Public Health Genomics Department of Public Health Sciences University of Virginia Charlottesville VA USA Department of Populat

Colorectal cancer (CRC) is a leading cause of mortality worldwide. We conducted a genome-wide association study meta-analysis of 100,204 CRC cases and 154,587 controls of European and east Asian ancestry, identifying 205 independent risk associations, of which 50 were unreported. We performed integrative genomic, transcriptomic and methylomic analyses across large bowel mucosa and other tissues. Transcriptome- and methylome-wide association studies revealed an additional 53 risk associations. We identified 155 high-confidence effector genes functionally linked to CRC risk, many of which had no previously established role in CRC. These have multiple different functions and specifically indicate that variation in normal colorectal homeostasis, proliferation, cell adhesion, migration, immunity and microbial interactions determines CRC risk. Crosstissue analyses indicated that over a third of effector genes most probably act outside the colonic mucosa. Our findings provide insights into colorectal oncogenesis and highlight potential targets across tissues for new CRC treatment and chemoprevention strategies.

关键词： Colorectal cancer Epigenomics Genome-wide association studies Transcriptomics

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Structure-Based Experimental Datasets for Benchmarking of Protein Simulation Force Fields

arXiv

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arXiv 2023年

作者： Cavender, Chapin E. Case, David A. Chen, Julian C.-H. Chong, Lillian T. Keedy, Daniel A. Lindorff-Larsen, Kresten Mobley, David L. Ollila, O.H. Samuli Oostenbrink, Chris Robustelli, Paul Voelz, Vincent A. Wall, Michael E. Wych, David C. Gilson, Michael K. Skaggs School of Pharmacy and Pharmaceutical Sciences UC San Diego 9500 Gilman Drive La Jolla CA92093 United States Dept. of Chemistry & Chemical Biology Rutgers University PiscatawayNJ08854 United States Bioscience Division Los Alamos National Laboratory Los Alamos NM87545 United States Department of Chemistry and Biochemistry The University of Toledo ToledoOH43606 United States University of Pittsburgh 219 Parkman Avenue PittsburghPA15260 United States Structural Biology Initiative CUNY Advanced Science Research Center New YorkNY10031 United States Department of Chemistry and Biochemistry City College of New York New YorkNY10031 United States PhD Programs in Biochemistry Biology and Chemistry CUNY Graduate Center New YorkNY10016 United States Linderstrøm-Lang Centre for Protein Science Department of Biology University of Copenhagen Copenhagen NDK-2200 Denmark Pharmaceutical Sciences Department University of California Irvine IrvineCA United States University of Helsinki Institute of Biotechnology Helsinki Finland Institute for Molecular Modeling and Simulation University of Natural Resources and Life Sciences Muthgasse 11 Vienna1190 Austria Dartmouth College Department of Chemistry HanoverNH03755 United States Department of Chemistry Temple University PhiladelphiaPA United States Computer Computational and Statistical Sciences Division Los Alamos National Laboratory Los AlamosNM87545 United States The Center for Nonlinear Studies Los Alamos National Laboratory Los AlamosNM87545 United States

This review article provides an overview of structurally oriented, experimental datasets that can be used to benchmark protein force fields, focusing on data generated by nuclear magnetic resonance (NMR) spectroscopy and room temperature (RT) protein crystallography. We discuss why these observables are useful for assessing force field accuracy, how they can be calculated from simulation trajectories, and statistical issues that arise when comparing simulations with experiment. The target audience for this article is computational researchers and trainees who develop, benchmark, or use protein force fields for molecular simulations. © 2023, CC BY.

关键词： Proteins

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Democratising Knowledge Representation with BioCypher

arXiv

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arXiv 2022年

作者： Lobentanzer, Sebastian Aloy, Patrick Baumbach, Jan Bohar, Balazs Charoentong, Pornpimol Danhauser, Katharina Doğan, Tunca Dreo, Johann Dunham, Ian Fernandez-Torras, Adrià Gyori, Benjamin M. Hartung, Michael Hoyt, Charles Tapley Klein, Christoph Korcsmaros, Tamas Maier, Andreas Mann, Matthias Ochoa, David Pareja-Lorente, Elena Popp, Ferdinand Preusse, Martin Probul, Niklas Schwikowski, Benno Sen, Bünyamin Strauss, Maximilian T. Turei, Denes Ulusoy, Erva Heidrun Wodke, Judith Andrea Saez-Rodriguez, Julio Heidelberg University Faculty of Medicine Heidelberg University Hospital Institute for Computational Biomedicine Bioquant Heidelberg Germany The Barcelona Institute of Science and Technology Catalonia Barcelona Spain Catalonia Barcelona Spain Institute for Computational Systems Biology University of Hamburg Germany Earlham Institute Norwich United Kingdom Biological Research Centre Szeged Hungary Heidelberg University Im Neuenheimer Feld 267 Heidelberg69120 Germany Im Neuenheimer Feld 460 Heidelberg69120 Germany Department of Pediatrics Dr. von Hauner Children’s Hospital University Hospital LMU Munich Germany Biological Data Science Lab Department of Computer Engineering Hacettepe University Ankara Turkey Department of Bioinformatics Graduate School of Health Sciences Hacettepe University Ankara Turkey Computational Systems Biomedicine Lab Department of Computational Biology Institut Pasteur Université Paris Cité Paris France Bioinformatics and Biostatistics Hub Institut Pasteur Université Paris Cité Paris France Wellcome Genome Campus Cambridgeshire HinxtonCB10 1SD United Kingdom Open Targets Wellcome Genome Campus Cambridgeshire HinxtonCB10 1SD United Kingdom Laboratory of Systems Pharmacology Harvard Medical School Boston United States Imperial College London London United Kingdom Quadram Institute Bioscience Norwich United Kingdom Proteomics Program Novo Nordisk Foundation Centre for Protein Research University of Copenhagen Copenhagen Denmark Department of Proteomics and Signal Transduction Max Planck Institute of Biochemistry Martinsried Germany Im Neuenheimer Feld 460 Heidelberg69120 Germany Neuherberg Germany Medical Informatics Laboratory University Medicine Greifswald Germany

Standardising the representation of biomedical knowledge among all researchers is an insurmountable task, hindering the effectiveness of many computational methods. To facilitate harmonisation and interoperability despite this fundamental challenge, we propose to standardise the framework of knowledge graph creation instead. We implement this standardisation in BioCypher, a FAIR (findable, accessible, interoperable, reusable) framework to transparently build biomedical knowledge graphs while preserving provenances of the source data. Mapping the knowledge onto biomedical ontologies helps to balance the needs for harmonisation, human and machine readability, and ease of use and accessibility to nonspecialist researchers. We demonstrate the usefulness of this framework on a variety of use cases, from maintenance of task-specific knowledge stores, to interoperability between biomedical domains, to on-demand building of task-specific knowledge graphs for federated learning. BioCypher (https://***) frees up valuable developer time;we encourage further development and usage by the community. © 2022, CC BY-NC-ND.

关键词： Ontology

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