DSEATM: drug set enrichment analysis uncovering disease mechanisms by biomedical text mining.Disease pathogenesis is always a major topic in biomedical research.  On this basis, we proposed a 'Drug Set Enrichment Analysis by Text Mining (DSEATM)' pipeline and applied it to 3250 diseases, which outperformed the state-of-the-art method.  Furthermore, diseases pathways enriched by DSEATM were similar to those obtained using the TCGA cancer RNA-seq differentially expressed genes.  In addition, the drug number, which showed a remarkable positive correlation of 0.73 with the AUC, plays a determining role in the performance of DSEATM.  Taken together, DSEATM is an auspicious and accurate disease research tool that offers fresh insights.
DSEATM
DSEATM: drug set enrichment analysis uncovering disease mechanisms by biomedical text mining.Disease pathogenesis is always a major topic in biomedical research.  On this basis, we proposed a 'Drug Set Enrichment Analysis by Text Mining (DSEATM)' pipeline and applied it to 3250 diseases, which outperformed the state-of-the-art method.  Furthermore, diseases pathways enriched by DSEATM were similar to those obtained using the TCGA cancer RNA-seq differentially expressed genes.  In addition, the drug number, which showed a remarkable positive correlation of 0.73 with the AUC, plays a determining role in the performance of DSEATM.  Taken together, DSEATM is an auspicious and accurate disease research tool that offers fresh insights.
DSEATM