Work flow of the multi-methodological approach.

Step 1, sequence symmetry analysis (SSA) of prescriptions or claims database and disproportionality analysis (DPA) of spontaneous adverse drug-event reports database to detect inverse signals and drug-repositioning signal. An inverse signal indicates an inverse association between the number of prescription drugs and the diagnosis of the associated diseases. The drug-repositioning signal indicates the therapeutic benefit revealed by the inverse signal identified using SSA and DPA; Step 2, bioinformatics analysis of data from open sources to cross-validate the drug-repositioning signal. Gene expression data for diseases and their related drugs, extracted from open sources (Gene Expression Omnibus, GEO) as a bioset containing differentially expressed genes (DEGs). Meta-analysis discovered commonly perturbed biogroups (i.e., pathways) among biosets. Connectivity MAP (CMAP) analysis identified related compounds with similar gene signatures as extracted biosets; Step 3, literature mining and curation to generate drug-repositioning possibilities. Common biogroups and associated compounds identified in step 2 were profiled by literature mining for supportive evidence and the underlying mechanism of action (MOA) to generate drug-repositioning signals.