DDCM: A Computational Strategy for Drug Repositioning Based on Support-Vector Regression Algorithm

Autor: Manyi Xu, Wan Li, Jiaheng He, Yahui Wang, Junjie Lv, Weiming He, Lina Chen, Hui Zhi
Jazyk: angličtina
Rok vydání: 2024
Předmět:
Zdroj: International Journal of Molecular Sciences, Vol 25, Iss 10, p 5267 (2024)
Druh dokumentu: article
ISSN: 1422-0067
1661-6596
DOI: 10.3390/ijms25105267
Popis: Computational drug-repositioning technology is an effective tool for speeding up drug development. As biological data resources continue to grow, it becomes more important to find effective methods to identify potential therapeutic drugs for diseases. The effective use of valuable data has become a more rational and efficient approach to drug repositioning. The disease–drug correlation method (DDCM) proposed in this study is a novel approach that integrates data from multiple sources and different levels to predict potential treatments for diseases, utilizing support-vector regression (SVR). The DDCM approach resulted in potential therapeutic drugs for neoplasms and cardiovascular diseases by constructing a correlation hybrid matrix containing the respective similarities of drugs and diseases, implementing the SVR algorithm to predict the correlation scores, and undergoing a randomized perturbation and stepwise screening pipeline. Some potential therapeutic drugs were predicted by this approach. The potential therapeutic ability of these drugs has been well-validated in terms of the literature, function, drug target, and survival-essential genes. The method’s feasibility was confirmed by comparing the predicted results with the classical method and conducting a co-drug analysis of the sub-branch. Our method challenges the conventional approach to studying disease–drug correlations and presents a fresh perspective for understanding the pathogenesis of diseases.
Databáze: Directory of Open Access Journals
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