Featured Article

Genotation: Actionable knowledge for the scientific reader

Panduka Nagahawatte, Ethan Willis, Mark Sakauye, Rony Jose, Hao Chen, Robert L. Davis

Research output: Contribution to journalArticle

Abstract

We present an article viewer application that allows a scientific reader to easily discover and share knowledge by linking genomics-related concepts to knowledge of disparate biomedical databases. High-throughput data streams generated by technical advancements have contributed to scientific knowledge discovery at an unprecedented rate. Biomedical Informaticists have created a diverse set of databases to store and retrieve the discovered knowledge. The diversity and abundance of such resources present biomedical researchers a challenge with knowledge discovery. These challenges highlight a need for a better informatics solution. We use a text mining algorithm, Genomine, to identify gene symbols from the text of a journal article. The identified symbols are supplemented with information from the GenoDB knowledgebase. Self-updating GenoDB contains information from NCBI Gene, Clinvar, Medgen, dbSNP, KEGG, PharmGKB, Uniprot, and Hugo Gene databases. The journal viewer is a web application accessible via a web browser. The features described herein are accessible on www.genotation.org. The Genomine algorithm identifies gene symbols with an accuracy shown by.65 F-Score. GenoDB currently contains information regarding 59,905 gene symbols, 5633 drug–gene relationships, 5981 gene–disease relationships, and 713 pathways. This application provides scientific readers with actionable knowledge related to concepts of a manuscript. The reader will be able to save and share supplements to be visualized in a graphical manner. This provides convenient access to details of complex biological phenomena, enabling biomedical researchers to generate novel hypothesis to further our knowledge in human health. This manuscript presents a novel application that integrates genomic, proteomic, and pharmacogenomic information to supplement content of a biomedical manuscript and enable readers to automatically discover actionable knowledge.

Original languageEnglish (US)
Pages (from-to)1202-1209
Number of pages8
JournalExperimental Biology and Medicine
Volume241
Issue number11
DOIs
StatePublished - Jun 1 2016

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Genes
Manuscripts
Databases
Data mining
Web Browser
Research Personnel
Biological Phenomena
Informatics
Data Mining
Web browsers
Knowledge Bases
Pharmacogenetics
Genomics
World Wide Web
Proteomics
Throughput
Health

All Science Journal Classification (ASJC) codes

  • Biochemistry, Genetics and Molecular Biology(all)

Cite this

Featured Article : Genotation: Actionable knowledge for the scientific reader. / Nagahawatte, Panduka; Willis, Ethan; Sakauye, Mark; Jose, Rony; Chen, Hao; Davis, Robert L.

In: Experimental Biology and Medicine, Vol. 241, No. 11, 01.06.2016, p. 1202-1209.

Research output: Contribution to journalArticle

Nagahawatte, Panduka ; Willis, Ethan ; Sakauye, Mark ; Jose, Rony ; Chen, Hao ; Davis, Robert L. / Featured Article : Genotation: Actionable knowledge for the scientific reader. In: Experimental Biology and Medicine. 2016 ; Vol. 241, No. 11. pp. 1202-1209.
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