Biology subjects

Riutta, A.

Publications and source records attributed to Riutta, A..

3 recordsLinked to original sources

bioRxiv2020

25 Years of Pathway Figures

BackgroundPathway diagrams are fundamental tools for describing biological processes in all aspects of science, including training, generating hypotheses, describing new knowledge and ultimately as communication tools in published work. Thousands of pathway diagrams are published each year as figures in papers. But as static images the pathway knowledge represented in figures is not accessible to researchers for computational queries and analyses. In this study, we aimed to identify pathway figures published in the past 25 years, to characterize the human gene content in figures by optical character recognition, and to describe their utility as a resource for pathway knowledge. ApproachTo identify pathway figures representing 25 years of published research, we trained a ma-chine learning service on manually-classified figures and applied it to 235,081 image query results from PubMed Central. Our previously described pipeline 1 was utilized to extract hu-man genes from the pathway figure images. These figures were characterized in terms of their parent papers, human gene content and enriched disease terms. Diverse use cases were explored for this newly accessible pathway resource. ResultsWe identified 64,643 pathway figures published between 1995 and 2019, depicting 1,112,551 instances of human genes (13,464 unique NCBI Genes) in various interactions and contexts. This represents more genes than found in the text of the same papers, as well as genes not found in any pathway database. We developed an interactive web tool to explore the results from the 65k set of figures, and used this tool to explore the history of scientific discovery of the Hippo Signaling pathway. We also defined a filtered set of 32k pathway figures useful for enrichment analysis.

bioinformatics↗

bioRxiv2019

Wikidata as a FAIR knowledge graph for the life sciences

Wikidata is a community-maintained knowledge base that epitomizes the FAIR principles of Findability, Accessibility, Interoperability, and Reusability. Here, we describe the breadth and depth of biomedical knowledge contained within Wikidata, assembled from primary knowledge repositories on genomics, proteomics, genetic variants, pathways, chemical compounds, and diseases. We built a collection of open-source tools that simplify the addition and synchronization of Wikidata with source databases. We furthermore demonstrate several use cases of how the continuously updated, crowd-contributed knowledge in Wikidata can be mined. These use cases cover a diverse cross section of biomedical analyses, from crowdsourced curation of biomedical ontologies, to phenotype-based diagnosis of disease, to drug repurposing.

bioinformatics↗

bioRxiv2018

Identifying Genes in Published Pathway Figure Images

BACKGROUNDPathway figures are commonly found in the biomedical literature providing intuitive models of complex processes in a visually concise format. The contents of a pathway figure often reflect the key findings and relevant context of an article. Unfortunately, the vast majority of pathway figures are drawn as one-off static images despite freely available pathway tools and resources, thus rendering their contents inaccessible to search, data mining and downstream analysis.\n\nAPPROACHLeveraging advances in optical character recognition and domain expertise in pathway modeling, we devised an approach to identify genes in published pathway figures. The approach was optimized against a set of figure images obtained from PubMed Central and tested against a set of 400 curated pathways with known content from WikiPathways (F-measure 95.2%).\n\nRESULTSApplied to 3982 published pathway figures spanning a four year period, our approach identified 29,189 gene symbols representing 4159 unique gene identifiers. The gene content unlocked from just this small sample of published figures includes novel and diverse pathway associations unmatched by any pathway database. Our approach over doubled the number of genes associated with the articles containing these figures as compared to combined annotations available from PubMed and PubTator. Encouraged by these initial results, we plan to scale the approach to make the molecular contents of the continuing stream of published pathway figures more accessible.

bioinformatics↗