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Drug-target interaction prediction based on protein features, using wrapper feature selection
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DOI：10.1038/s41598-023-30026-y.png)
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摘要
En 中文
Drug-target interaction prediction is a vital stage in drug development, involving lots of methods. Experimental methods that identify these relationships on the basis of clinical remedies are time-taking, costly, laborious, and complex introducing a lot of challenges. One group of new methods is called computational methods. The development of new computational methods which are more accurate can be preferable to experimental methods, in terms of total cost and time. In this paper, a new computational model to predict drug-target interaction (DTI), consisting of three phases, including feature extraction, feature selection, and classification is proposed. In feature extraction phase, different features such as EAAC, PSSM and etc. would be extracted from sequence of proteins and fingerprint features from drugs. These extracted features would then be combined. In the next step, one of the wrapper feature selection methods named IWSSR, due to the large amount of extracted data, is applied. The selected features are then given to rotation forest classification, to have a more efficient prediction. Actually, the innovation of our work is that we extract different features; and then select features by the use of IWSSR. The accuracy of the rotation forest classifier based on tenfold on the golden standard datasets (enzyme, ion channels, G-protein-coupled receptors, nuclear receptors) is as follows: 98.12, 98.07, 96.82, and 95.64. The results of experiments indicate that the proposed model has an acceptable rate in DTI prediction and is compatible with the proposed methods in other papers.
Keyword:
LEARNING-METHODS
GENE
KEGG
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期刊
IF:
3.9
论文数:
28.0W
被引数:
83.5W
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引用论文
Machine learning approaches and databases for prediction of drug-target interaction: a survey paper用于预测药物-靶标相互作用的机器学习方法和数据库: 调查论文OAAI
OAAI分享收藏Revealing Drug-Target Interactions with Computational Models and Algorithms用计算模型和算法揭示药物-靶标相互作用.png)
MOLECULESOAAI
MOLECULESIF4.6
OAAI分享收藏Predicting drug-target interactions using Lasso with random forest based on evolutionary information and chemical structure基于进化信息和化学结构的随机森林套索预测药物-靶标相互作用GENOMICSOAAI
GENOMICSIF3
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