title: Featured Article: Genotation: Actionable knowledge for the scientific reader.
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.
authors.affiliation: Center of Biomedical Informatics, Memphis, TN 38103, USA panduka@uthsc.edu.
authors.forename: Panduka
authors.initials: P
authors.lastname: Nagahawatte
authors.affiliation: Center of Biomedical Informatics, Memphis, TN 38103, USA.
authors.forename: Ethan
authors.initials: E
authors.lastname: Willis
authors.affiliation: Center of Biomedical Informatics, Memphis, TN 38103, USA.
authors.forename: Mark
authors.initials: M
authors.lastname: Sakauye
authors.affiliation: Center of Biomedical Informatics, Memphis, TN 38103, USA.
authors.forename: Rony
authors.initials: R
authors.lastname: Jose
authors.affiliation: Department of Pharmacology, University of Tennessee Health Science Centre, Memphis, TN 38103, USA.
authors.forename: Hao
authors.initials: H
authors.lastname: Chen
authors.affiliation: Center of Biomedical Informatics, Memphis, TN 38103, USA.
authors.forename: Robert L
authors.initials: RL
authors.lastname: Davis
mesh_terms: D000465:Algorithms; D035843:Biomedical Research / Q000379:methods; D019295:Computational Biology / Q000379:methods*; D057225:Data Mining / Q000379:methods*; D016208:Databases, Factual; D006801:Humans; D012984:Software
keywords: Genomic supplements; annotation gene; annotation protein; journal article viewer; pharmacogenomics