使用机器学习技术校准蛋白质序列预测分类器:一项实证研究

T. Idhaya, A. Suruliandi, D. Calitoiu, S. Raja
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引用次数: 1

摘要

基因是先天性特征的基本单位,也是脱氧核糖核酸中加密蛋白质合成的核苷酸序列。蛋白质由氨基酸残基组成,并被分类用于蛋白质相关研究,包括识别基因变化,发现与疾病和表型的关联,以及识别潜在的药物靶点。为此,人们根据家族对蛋白质进行了研究和分类。然而,对于家庭预测,由于后一过程所涉及的时间,采用计算方法而不是实验方法。蛋白质家族预测的计算方法涉及两个重要的过程:特征选择和分类。现有的蛋白质家族预测方法有基于比对和无比对两种。前者的缺点是它通过排列每个可用的序列来搜索蛋白质特征。因此,考虑到后者只需要基于序列的特征来预测蛋白质家族,并且比前者效率高得多,因此我们采用后者的无比对方法进行研究。然而,用于研究的基于序列的特征还提供了额外的功能。因此,有必要从所有特性中选择最佳特性。说到分类,对蛋白质的分类仍然没有完美的方法。在此基础上,对不同的方法进行了比较,以寻找最适合蛋白质家族预测的特征选择技术和分类技术。从研究结果来看,所选择的特征子集为基于滤波器的特征选择技术和随机森林分类器提供了96%的最佳分类准确率。
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Calibrating the classifier for protein family prediction with protein sequence using machine learning techniques: An empirical investigation
A gene is a basic unit of congenital traits and a sequence of nucleotides in deoxyribonucleic acid that encrypts protein synthesis. Proteins are made up of amino acid residue and are classified for use in protein-related research, which includes identifying changes in genes, finding associations with diseases and phenotypes, and identifying potential drug targets. To this end, proteins are studied and classified, based on the family. For family prediction, however, a computational rather than an experimental approach is introduced, owing to the time involved in the latter process. Computational approaches to protein family prediction involve two important processes, feature selection and classification. Existing approaches to protein family prediction are alignment-based and alignment-free. The drawback of the former is that it searches for protein signatures by aligning every available sequence. Consequently, the latter alignment-free approach is taken for study, given that it only needs sequence-based features to predict the protein family and is far more efficient than the former. Nevertheless, the sequence-based characteristics taken for study have additional features to offer. There is, thus, a need to select the best features of all. When comes to classification still there is no perfection in classifying the protein. So, a comparison of different approaches is done to find the best feature selection technique and classification technique for protein family prediction. From the study, the feature subset selected provides the best classification accuracy of 96% for filter-based feature selection technique and the random forest classifier.
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