Mining emerging biomedical literature for understanding disease associations in drug discovery.
Systematically evaluating the exponentially growing body of scientific literature has become a critical task that every drug discovery organization must engage in in order to understand emerging trends for scientific investment and strategy development. Developing trends analysis uses the number of publications within a 3-year window to determine concepts derived from well-established disease and gene ontologies to aid in recognizing and predicting emerging areas of scientific discoveries relevant to that space. In this chapter, we describe such a method and use obesity and psoriasis as use-case examples by analyzing the frequency of disease-related MeSH terms in PubMed abstracts over time. We share how our system can be used to predict emerging trends at a relatively early stage and we analyze the literature-identified genes for genetic associations, druggability, and biological pathways to explore any potential biological connections between the two diseases that could be utilized for drug discovery.
Computational Biology, GlaxoSmithKline R&D, Research Triangle Park, North Carolina, NC, USA.
Deepak K
DK
Rajpal
Xiaoyan A
XA
Qu
Johannes M
JM
Freudenberg
Vinod D
VD
Kumar
D000818:Animals; D057225:Data Mining / Q000379:methods*; D055808:Drug Discovery / Q000379:methods*; D063990:Gene Ontology; D006801:Humans; D063369:Knowledge Discovery / Q000379:methods*; D016239:MEDLINE; D046650:Medical Subject Headings