Diffusion enables integration of heterogeneous data and user-driven learning in a desktop knowledge-base.

Journal: PLoS computational biology
PMID: 34379637

Abstract

Integrating reference datasets (e.g. from high-throughput experiments) with unstructured and manually-assembled information (e.g. notes or comments from individual researchers) has the potential to tailor bioinformatic analyses to specific needs and to lead to new insights. However, developing bespoke analysis pipelines from scratch is time-consuming, and general tools for exploring such heterogeneous data are not available. We argue that by treating all data as text, a knowledge-base can accommodate a range of bioinformatic data types and applications. We show that a database coupled to nearest-neighbor algorithms can address common tasks such as gene-set analysis as well as specific tasks such as ontology translation. We further show that a mathematical transformation motivated by diffusion can be effective for exploration across heterogeneous datasets. Diffusion enables the knowledge-base to begin with a sparse query, impute more features, and find matches that would otherwise remain hidden. This can be used, for example, to map multi-modal queries consisting of gene symbols and phenotypes to descriptions of diseases. Diffusion also enables user-driven learning: when the knowledge-base cannot provide satisfactory search results in the first instance, users can improve the results in real-time by adding domain-specific knowledge. User-driven learning has implications for data management, integration, and curation.

Authors

  • Tomasz Konopka
    William Harvey Research Institute, Queen Mary University of London, London, United Kingdom.
  • Sandra Ng
    William Harvey Research Institute, Queen Mary University of London, London, United Kingdom.
  • Damian Smedley
    School of ITEE, The University of Queensland, St. Lucia, QLD 4072, Australia, Garvan Institute of Medical Research, Darlinghurst, Sydney, NSW 2010, Australia, Institute for Medical Genetics and Human Genetics, Charité-Universitätsmedizin Berlin, 13353 Berlin, Germany, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, UK, National Institute of Informatics, Hitotsubashi, Tokyo, Japan, Mouse Informatics Group, Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton CB10 1SA, UK, LASIGE, Departamento de Informática, Faculdade de Ciências, Universidade de Lisboa, 1749-016 Lisboa, Portugal, Genetic Services of Western Australia, King Edward Memorial Hospital, WA 6008, Australia, School of Paediatrics and Child Health, University of Western Australia, WA 6008, Australia, Institute for Immunology and Infectious Diseases, Murdoch University, WA 6150, Australia, Office of Population Health, Public Health and Clinical Services Division, Western Australian Department of Health, WA 6004, Australia, Academic Department of Medical Genetics, Sydney Children's Hospitals Network (Westmead), NSW 2145, Australia, Discipline of Genetic Medicine, Sydney Medical School, The University of Sydney, NSW 2006, Australia, Max Planck Institute for Molecular Genetics, 14195 Berlin, Germany, Institute for Bioinformatics, Department of Mathematics and Computer Science, Freie Universität Berlin, 14195 Berlin, Germany and Berlin Brandenburg Center for Regenerative Therapies, 13353 Berlin, Germany.

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