数字农科院2.0

DSEATM: drug set enrichment analysis uncovering disease mechanisms by biomedical text mining

文献类型： 外文期刊

作者： Luo, Zhi-Hui;Zhu, Li-Da;Wang, Ya-Min;Qian, Sheng Hu;Li, Menglu;Zhang, Wen;Chen, Zhen-Xia

作者机构：

关键词： drug set enrichment analysis;text mining;MESH;disease pathway

期刊名称： BRIEFINGS IN BIOINFORMATICS

ISSN： 1467-5463

年卷期： 2022 年

页码：

收录情况： SCIE（2022版）

摘要： Disease pathogenesis is always a major topic in biomedical research. With the exponential growth of biomedical information, drug effect analysis for specific phenotypes has shown great promise in uncovering disease-associated pathways. However, this method has only been applied to a limited number of drugs. Here, we extracted the data of 4634 diseases, 3671 drugs, 112 809 disease-drug associations and 81 527 drug-gene associations by text mining of 29 168 919 publications. 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.

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