Biology subjects

Mayers, M.

Publications and source records attributed to Mayers, M..

3 recordsLinked to original sources

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↗

bioRxiv2019

Structured Reviews for Data and Knowledge Driven Research

MotivationHypothesis generation is a critical step in research and a cornerstone in the rare disease field. Research is most efficient when those hypotheses are based on the entirety of knowledge known to date. Systematic review articles are commonly used in biomedicine to summarize existing knowledge and contextualize experimental data. But the information contained within review articles is typically only expressed as free-text, which is difficult to use computationally. Researchers struggle to navigate, collect and remix prior knowledge as it is scattered in several silos without seamless integration and access. This lack of a structured information framework hinders research by both experimental and computational scientists.\n\nResultsTo better organize knowledge and data, we built a structured review article that is specifically focused on NGLY1 Deficiency, an ultra-rare genetic disease first reported in 2012. We represented this structured review as a knowledge graph, and then stored this knowledge graph in a Neo4j database to simplify dissemination, querying, and visualization of the network. Relative to free-text, this structured review better promotes the principles of findability, accessibility, interoperability, and reusability (FAIR). In collaboration with domain experts in NGLY1 Deficiency, we demonstrate how this resource can improve the efficiency and comprehensiveness of hypothesis generation. We also developed a read-write interface that allows domain experts to contribute FAIR structured knowledge to this community resource. In contrast to traditional free-text review articles, this structured review exists as a living knowledge graph that is curated by humans and accessible to computational analyses. Finally, we have generalized this workflow into modular and repurposable components that can be applied to other domain areas. This NGLY1 Deficiency-focused network is publicly available at http://ngly1graph.org/.\n\nAvailability and implementationSource code and network data files are at: https://github.com/SuLab/ngly1-graph and https://github.com/SuLab/bioknowledge-reviewer.\n\nContactasu@scripps.edu

bioinformatics↗

bioRxiv2019

Applying Citizen Science to Gene, Drug, Disease Relationship Extraction from Biomedical Abstracts.

Biomedical literature is growing at a rate that outpaces our ability to harness the knowledge contained therein. In order to mine valuable inferences from the large volume of literature, many researchers have turned to information extraction algorithms to harvest information in biomedical texts. Information extraction is usually accomplished via a combination of manual expert curation and computational methods. Advances in computational methods usually depends on the generation of gold standards by a limited number of expert curators. This process can be time consuming and represents an area of biomedical research that is ripe for exploration with citizen science. Citizen scientists have been previously found to be willing and capable of performing named entity recognition of disease mentions in biomedical abstracts, but it was uncertain whether or not the same could be said of relationship extraction. Relationship extraction requires training on identifying named entities as well as a deeper understanding of how different entity types can relate to one another. Here, we used the web-based application Mark2Cure (https://mark2cure.org) to demonstrate that citizen scientists can perform relationship extraction and confirm the importance of accurate named entity recognition on this task. We also discuss opportunities for future improvement of this system, as well as the potential synergies between citizen science, manual biocuration, and natural language processing.

bioinformatics↗