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.

中文翻译：

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DSEATM：通过生物医学文本挖掘揭示疾病机制的药物集富集分析。

疾病发病机制一直是生物医学研究的主要课题。随着生物医学信息的指数级增长，针对特定表型的药物效应分析在揭示疾病相关途径方面显示出巨大的希望。然而，这种方法只适用于有限数量的药物。在这里，我们通过对 29 168 919 篇出版物的文本挖掘，提取了 4634 种疾病、3671 种药物、112 809 种疾病-药物关联和 81 527 种药物-基因关联的数据。在此基础上，我们提出了“文本挖掘的药物集富集分析 (DSEATM)”管道，并将其应用于 3250 种疾病，其性能优于最先进的方法。此外，由 DSEATM 富集的疾病途径与使用 TCGA 癌症 RNA-seq 差异表达基因获得的疾病途径相似。另外，药号，与 AUC 呈 0.73 的显着正相关，对 DSEATM 的性能起着决定性的作用。总而言之，DSEATM 是一种吉祥而准确的疾病研究工具，提供了新的见解。