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

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...

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Vydáno v:International journal of molecular sciences Ročník 25; číslo 10; s. 5267
Hlavní autoři: Xu, Manyi, Li, Wan, He, Jiaheng, Wang, Yahui, Lv, Junjie, He, Weiming, Chen, Lina, Zhi, Hui
Médium: Journal Article
Jazyk:angličtina
Vydáno: Switzerland MDPI AG 01.05.2024
Témata:
Algorithms
Analysis
Anopheles
Cardiovascular diseases
Cardiovascular Diseases - drug therapy
Computational Biology - methods
Data mining
Development and progression
Disease
Drug development
Drug Repositioning - methods
Drug therapy
Drugs
Genes
Health aspects
Humans
Lung cancer
Machine learning
Medical research
Medicine, Experimental
Neoplasms - drug therapy
Pathogenesis
Pharmaceutical industry
Romidepsin
Support Vector Machine
Survival analysis
Tumors
hybrid matrix
drug repositioning
support-vector regression
potential therapeutic drugs
ISSN:1422-0067, 1661-6596, 1422-0067
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Shrnutí: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.
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ISSN:1422-0067
1661-6596
1422-0067
DOI:10.3390/ijms25105267