TargetAnalytica: A Text Analytics Framework for Ranking Therapeutic Molecules in the Bibliome

Biomedical scientists often search databases of therapeutic molecules to answer a set of molecule-related questions. When it comes to drugs, finding the most specific target is a crucial biological criterion. Whether the target is a gene, protein, and cell line, target specificity is what makes a therapeutic molecule significant. In this chapter, we present TargetAnalytica, a novel text analytics framework that is concerned with mining the biomedical literature. Starting with a set of publications of interest, the framework produces a set of biological entities related to gene, protein, RNA, cell type, and cell line. The framework is tested against a depression-related dataset for the purpose of demonstration. The analysis shows an interesting ranking that is significantly different from a counterpart based on drugs.com’s popularity factor (e.g., according to our analysis Cymbalta appears only at position #10 though it is number one in popularity according to the database). The framework is a crucial tool that identifies the targets to investigate, provides relevant specificity insights, and help decision makers and scientists to answer critical questions that are not possible otherwise.