Featured Article: Genotation: Actionable knowledge for the scientific reader

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 supp...
Source: Experimental Biology and Medicine - Category: Research Authors: Tags: Genomics, Proteomics and Bioinformatics Source Type: research