Democratising Knowledge Representation with BioCypher

Democratising Knowledge Representation with BioCypher Sebastian Lobentanzer, Patrick Aloy, Jan Baumbach, Balazs Bohar, Katharina Danhauser, Tunca Doğan, Johann Dreo, Ian Dunham, Adrià Fernandez-Torras, Benjamin M. Gyori, Michael Hartung, Charles Tapley Hoyt, Christoph Klein, Tamas Korcsmaros, Andreas Maier, Matthias Mann, David Ochoa, Elena Pareja-Lorente, Martin Preusse, Niklas Probul, Benno Schwikowski, Bünyamin Sen, Maximilian T. Strauss, Denes Turei, Erva Ulusoy, Judith Andrea Heidrun Wodke, Julio SaezRodriguez 1 Heidelberg University, Faculty of Medicine, and Heidelberg University Hospital, Institute for Computational Biomedicine, Bioquant, Heidelberg, Germany

[1]  I. Kohane,et al.  Deep learning for diagnosing patients with rare genetic diseases , 2022, medRxiv.

[2]  D. Castelvecchi.  Are ChatGPT and AlphaCode going to replace programmers? , 2022, Nature.

[3]  D. Koslicki,et al.  Predicting Drug Repurposing Candidates and Their Mechanisms from A Biomedical Knowledge Graph , 2022, ArXiv.

[4]  Jie Liu,et al.  GenomicKB: a knowledge graph for the human genome , 2022, Nucleic Acids Res..

[5]  Michelle M. Li,et al.  Graph representation learning in biomedicine and healthcare , 2022, Nature Biomedical Engineering.

[6]  Benjamin M. Gyori,et al.  Unifying the identification of biomedical entities with the Bioregistry , 2022, bioRxiv.

[7]  Christopher D. Manning,et al.  Deep Bidirectional Language-Knowledge Graph Pretraining , 2022, NeurIPS.

[8]  A. Jarasch,et al.  CovidGraph: a graph to fight COVID-19 , 2022, Bioinform..

[9]  T. Korcsmáros,et al.  Sherlock: an open-source data platform to store, analyze and integrate Big Data for computational biologists , 2022, F1000Research.

[10]  Yusuf H. Roohani,et al.  GEARS: Predicting transcriptional outcomes of novel multi-gene perturbations , 2022, bioRxiv.

[11]  M. Zitnik,et al.  Building a knowledge graph to enable precision medicine , 2022, bioRxiv.

[12]  Matthew H. Brush,et al.  Biolink Model: A universal schema for knowledge graphs in clinical, biomedical, and translational science , 2022, Clinical and translational science.

[13]  Fabian J Theis,et al.  Biologically informed deep learning to infer gene program activity in single cells , 2022, bioRxiv.

[14]  Maximilian T. Strauss,et al.  A knowledge graph to interpret clinical proteomics data , 2022, Nature Biotechnology.

[15]  James B. Munro,et al.  The Human Disease Ontology 2022 update , 2021, Nucleic Acids Res..

[16]  Steven J. M. Jones,et al.  A platform for oncogenomic reporting and interpretation , 2021, Nature Communications.

[17]  A. Bender,et al.  A Review of Biomedical Datasets Relating to Drug Discovery: A Knowledge Graph Perspective , 2021, Briefings Bioinform..

[18]  P. Aloy,et al.  Integrating and formatting biomedical data in the Bioteque, a comprehensive repository of pre-calculated knowledge graph embeddings , 2022 .

[19]  A. Nikolov,et al.  Biological Insights Knowledge Graph: an integrated knowledge graph to support drug development , 2021, bioRxiv.

[20]  J. Roach,et al.  RTX-KG2: a system for building a semantically standardized knowledge graph for translational biomedicine , 2021, bioRxiv.

[21]  C. Mungall,et al.  GraPE: fast and scalable Graph Processing and Embedding , 2021, ArXiv.

[22]  I. Dunham,et al.  Network expansion of genetic associations defines a pleiotropy map of human cell biology , 2021, bioRxiv.

[23]  David B. Blumenthal,et al.  On the Privacy of Federated Pipelines , 2021, SIGIR.

[24]  R. Cetin-Atalay,et al.  CROssBAR: comprehensive resource of biomedical relations with knowledge graph representations , 2021, Nucleic acids research.

[25]  Reza Nasirigerdeh,et al.  The FeatureCloud AI Store for Federated Learning in Biomedicine and Beyond , 2021, ArXiv.

[26]  Benjamin E. Gross,et al.  OncoTree: A Cancer Classification System for Precision Oncology. , 2021, JCO clinical cancer informatics.

[27]  Nadezhda T. Doncheva,et al.  The STRING database in 2021: customizable protein–protein networks, and functional characterization of user-uploaded gene/measurement sets , 2020, Nucleic Acids Res..

[28]  Kara Dolinski,et al.  The BioGRID database: A comprehensive biomedical resource of curated protein, genetic, and chemical interactions , 2020, Protein science : a publication of the Protein Society.

[29]  Peter N. Robinson,et al.  KG-COVID-19: A Framework to Produce Customized Knowledge Graphs for COVID-19 Response , 2020, bioRxiv.

[30]  Tamás Korcsmáros,et al.  Integrated intra- and intercellular signaling knowledge for multicellular omics analysis , 2020, bioRxiv.

[31]  Hiroaki Kitano,et al.  SBML Level 3: an extensible format for the exchange and reuse of biological models , 2020, Molecular systems biology.

[32]  C. von Kalle,et al.  The German Corona Consensus Dataset (GECCO): a standardized dataset for COVID-19 research in university medicine and beyond , 2020, BMC Medical Informatics and Decision Making.

[33]  S. Fröhling,et al.  Support systems to guide clinical decision-making in precision oncology: The Cancer Core Europe Molecular Tumor Board Portal , 2020, Nature Medicine.

[34]  Joshua M. Dempster,et al.  Integrated cross-study datasets of genetic dependencies in cancer , 2020, Nature Communications.

[35]  William A. Baumgartner,et al.  A Framework for Automated Construction of Heterogeneous Large-Scale Biomedical Knowledge Graphs , 2020, bioRxiv.

[36]  Aurelien Dugourd,et al.  Causal integration of multi‐omics data with prior knowledge to generate mechanistic hypotheses , 2020, bioRxiv.

[37]  H. Soreq,et al.  Integrative Transcriptomics Reveals Sexually Dimorphic Control of the Cholinergic/Neurokine Interface in Schizophrenia and Bipolar Disorder , 2019, Cell reports.

[38]  Benjamin M. Good,et al.  Gene Ontology Causal Activity Modeling (GO-CAM) moves beyond GO annotations to structured descriptions of biological functions and systems , 2019, Nature Genetics.

[39]  Martin Hofmann-Apitius,et al.  Integration of Structured Biological Data Sources using Biological Expression Language , 2019, bioRxiv.

[40]  Jan Eric Lenssen,et al.  Fast Graph Representation Learning with PyTorch Geometric , 2019, ArXiv.

[41]  C. Schade-Brittinger,et al.  MIRACUM: Medical Informatics in Research and Care in University Medicine , 2018, Methods of information in medicine.

[42]  Anil Kumar Sharma,et al.  Cancer molecular markers: A guide to cancer detection and management. , 2018, Seminars in cancer biology.

[43]  Lawrence Hunter,et al.  Knowledge-based biomedical Data Science , 2017, Data Sci..

[44]  Michael P. Schroeder,et al.  Cancer Genome Interpreter annotates the biological and clinical relevance of tumor alterations , 2017, Genome Medicine.

[45]  Gautier Koscielny,et al.  Open Targets: a platform for therapeutic target identification and validation , 2016, Nucleic Acids Res..

[46]  Julio Saez-Rodriguez,et al.  OmniPath: guidelines and gateway for literature-curated signaling pathway resources , 2016, Nature Methods.

[47]  Erik Schultes,et al.  The FAIR Guiding Principles for scientific data management and stewardship , 2016, Scientific Data.

[48]  Gary D. Bader,et al.  Promoting Coordinated Development of Community-Based Information Standards for Modeling in Biology: The COMBINE Initiative , 2015, Front. Bioeng. Biotechnol..

[49]  Mingming Jia,et al.  COSMIC: exploring the world's knowledge of somatic mutations in human cancer , 2014, Nucleic Acids Res..

[50]  The Uniprot Consortium,et al.  UniProt: a hub for protein information , 2014, Nucleic Acids Res..

[51]  S. Weber,et al.  German approach of coding rare diseases with ICD-10-GM and Orpha numbers in routine settings , 2014, Orphanet Journal of Rare Diseases.

[52]  Sylvia Stracke,et al.  Cohort profile: Greifswald approach to individualized medicine (GANI_MED) , 2014, Journal of Translational Medicine.

[53]  T. Ideker,et al.  Siri of the Cell: What Biology Could Learn from the iPhone , 2014, Cell.

[54]  Ted Slater,et al.  Recent advances in modeling languages for pathway maps and computable biological networks. , 2014, Drug discovery today.

[55]  Marko A. Rodriguez,et al.  The Graph Traversal Pattern , 2010, Graph Data Management.

[56]  Gary D Bader,et al.  BioPAX – A community standard for pathway data sharing , 2010, Nature Biotechnology.

[57]  S. Mundlos,et al.  The Human Phenotype Ontology , 2010, Clinical genetics.

[58]  Christopher G. Chute,et al.  BioPortal: ontologies and integrated data resources at the click of a mouse , 2009, Nucleic Acids Res..

[59]  M. Cornel,et al.  [Orphanet: a European database for rare diseases]. , 2008, Nederlands tijdschrift voor geneeskunde.

[60]  M. Ashburner,et al.  The OBO Foundry: coordinated evolution of ontologies to support biomedical data integration , 2007, Nature Biotechnology.

[61]  Nicholas C. Ide,et al.  Issues in the registration of clinical trials. , 2007, JAMA.

[62]  Kevin Donnelly,et al.  SNOMED-CT: The advanced terminology and coding system for eHealth. , 2006, Studies in health technology and informatics.

[63]  Patrick Lambrix,et al.  Representations of molecular pathways: an evaluation of SBML, PSI MI and BioPAX , 2005, Bioinform..

[64]  U. John,et al.  Study of Health in Pomerania (SHIP): A health examination survey in an east German region: Objectives and design , 2005, Sozial- und Präventivmedizin.

[65]  R. Durbin,et al.  The Sequence Ontology: a tool for the unification of genome annotations , 2005, Genome Biology.

[66]  C. Sander,et al.  The HUPO PSI's Molecular Interaction format—a community standard for the representation of protein interaction data , 2004, Nature Biotechnology.

[67]  Martin Vingron,et al.  IntAct: an open source molecular interaction database , 2004, Nucleic Acids Res..