Discriminating plasma membrane, internal membrane, and organelle membrane proteins by SVM

Mehwish Faiz, A. Ahmed, S. Abid
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Abstract

Different membrane proteins are associated with the bio-membrane of distinct organelles which characterize their activities in and out of the cell based on their locale. Recognition of the cellular alcove is thus crucial and can be executed through machine learning algorithms. We implemented a Support Vector Machine(SVM) in order to reveal the type of membrane protein by taking the Mem Loci dataset. Before implementing SVM, we transformed the amino acid sequence into Pseudo amino acid composition and this yields an accuracy of 74%. For output, we have to enter the PseAAC of proteins and the screen reveal the category of membrane protein as either internal membrane protein or organelle membrane protein, or plasma membrane protein.
利用支持向量机对质膜、细胞膜和细胞器膜蛋白进行区分
不同的膜蛋白与不同细胞器的生物膜相关联,这些细胞器根据它们的位置来表征它们在细胞内外的活动。因此,细胞凹室的识别是至关重要的,可以通过机器学习算法来执行。为了揭示膜蛋白的类型,我们实现了支持向量机(SVM),以Mem位点数据集为基础。在实现支持向量机之前,我们将氨基酸序列转换为伪氨基酸组成,这产生了74%的准确率。为了输出,我们必须进入蛋白质的PseAAC,屏幕显示膜蛋白的类别为内部膜蛋白或细胞器膜蛋白或质膜蛋白。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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