Re-curation and rational enrichment of knowledge graphs in Biological Expression Language

Abstract

The rapid accumulation of new biomedical literature not only causes curated knowledge graphs (KGs) to become outdated and incomplete, but also makes manual curation an impractical and unsustainable solution. Automated or semi-automated workflows are necessary to assist in prioritizing and curating the literature to update and enrich KGs. We have developed two workflows: one for re-curating a given KG to assure its syntactic and semantic quality and another for rationally enriching it by manually revising automatically extracted relations for nodes with low information density. We applied these workflows to the KGs encoded in Biological Expression Language from the NeuroMMSig database using content that was pre-extracted from MEDLINE abstracts and PubMed Central full-text articles using text mining output integrated by INDRA. We have made this workflow freely available at https://github.com/bel-enrichment/bel-enrichment .

The Dark Kinase Knowledgebase: an online compendium of knowledge and experimental results of understudied kinases

“…We have similarly summarized TCGA data for each kinase through heatmaps that provide a visualization on mutations, copy number variation and mRNA expression levels of the kinase across multiple cancer types. We are further providing functional interaction networks for each dark kinase, for example using data integrated through INDRA ( 1 , 28 , 29 ). A summary of the highlights from our Dark Kinase expression browser (described further below) is also displayed with corresponding links to the full application.…”

The role of metadata in reproducible computational research

“…Further, there are several organizations 186 , 187 , 188 , 189 , 190 whose purpose is to curate and formalize the scientific literature in these formats and distribute them in one of several databases and repositories. Because curation is both difficult and time-consuming, several semi-automated NLP 191 , 192 curation workflows based on NLP-based relation extraction systems 193 , 194 , 195 and assemblers 196 have been proposed to assist.…”

Leveraging Structured Biological Knowledge for Counterfactual Inference: A Case Study of Viral Pathogenesis

“…In contrast, the Biological Expression Language (BEL) [20] was specifically designed for manual extraction and automated integration of author statements about causal relationships among biological entities, biological processes, and cellular-level observable phenomena [11]. The syntax of a BEL statement is comprised of a triple in the form of {subject, predicate, object}.…”

“…It represents prior domain knowledge in terms of causal diagrams, assumes a probability distribution on exogenous variables, and assigns a deterministic function to endogenous variables. SCM are particularly attractive in systems biology, where structured domain knowledge is extracted from the biomedical literature and is readily available through advances in natural language processing [7], [8], [9], large-scale automated assembly systems [10], and semi-automated curation workflows [11]. This knowledge is curated by multiple organizations [12], [13], [14], [15], [16] and stored in structured knowledge bases [17], [18], [19], [20].…”