Vladimir Volynkin

TL;DR: An annotation score for all entries in UniProt is introduced to represent the relative amount of knowledge known about each protein to help identify which proteins are the best characterized and most informative for comparative analysis.

TL;DR: The UniProtKB responded to the COVID-19 pandemic through expert curation of relevant entries that were rapidly made available to the research community through a dedicated portal and a credit-based publication submission interface was developed.

TL;DR: UniRule provides scalable enrichment of annotation in UniProtKB, a method of annotation based on expertly curated rules, which integrates related systems (RuleBase, HAMAP, PirSR, PIRNR) developed by the members of the UniProt consortium.

TL;DR: CROssBAR as mentioned in this paper is a comprehensive system that integrates large-scale biological/biomedical data from various resources and stores them in a NoSQL database, enriched with the deep-learning-based prediction of relationships between numerous data entries, which is followed by the rigorous analysis of the enriched data to obtain biologically meaningful modules.

TL;DR: A comprehensive system that integrates large-scale biomedical data from various resources and store them in a new NoSQL database, enrich these data with deep-learning-based prediction of relations between numerous biomedical entities, rigorously analyse the enriched data to obtain biologically meaningful modules and display them to users via easy-to-interpret, interactive and heterogenous knowledge graph (KG) representations.