Support vector machines for prediction of protein subcellular location
Support vector machines for prediction of protein subcellular location
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DOI：
10.1006/mcbr.2001.0285
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发表时间：
2000-10-01
期刊：
影响因子：
--
通讯作者：
Chou, Kuo-Chen
中科院分区：
文献类型：
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作者：
Cai, Yu-Dong;Liu, Xiao-Jun;Chou, Kuo-Chen
将支持向量机作为一种学习机器，利用蛋白质的氨基酸组成来预测蛋白质的亚细胞位置。在本研究中，蛋白质分为以下12类：(1)叶绿体，(2)细胞质，(3)细胞骨架，(4)内质网，(5)外壁，(6)高尔基体，(7)溶酶体，(8)线粒体，(9)核，(10)过氧体，(11)质膜，(12)液泡，几乎覆盖了动植物细胞中所有的细胞器和亚细胞间隔。对三组蛋白质：2022个蛋白质、2161个蛋白质和2319个蛋白质进行了支持向量机方法的自一致性检验和刀切检验。结果表明，2 022个蛋白质的自洽正确率和刀切检验正确率分别达到91%和82%，和2 161个蛋白质的正确率分别达到75%和85%，2 319个蛋白质的正确率达到73%。此外，使用包含2240个蛋白质、2513个蛋白质和2591个蛋白质的三个独立测试数据集对预测速度进行了检验。对2240个蛋白质、2513个蛋白质和2591个蛋白质的正确预测率分别达到82%、75%和73%。
Support Vector Machine (SVM), which is one kind of learning machines, was applied to predict the subcellular location of proteins from their amino acid composition. In this research, the proteins are classified into the following 12 groups: (1) chloroplast, (2) cytoplasm, (3) cytoskeleton, (4) endoplasmic reticulum, (5) extracall, (6) Golgi apparatus, (7) lysosome, (8) mitochondria, (9) nucleus, (10) peroxisome, (11) plasma membrane, and (12) vacuole, which have covered almost all the organelles and subcellular compartments in an animal or plant cell. The examination for the self-consistency and the jackknife test of the SVMs method was tested for the three sets: 2022 proteins, 2161 proteins, and 2319 proteins. As a result, the correct rate of self-consistency and jackknife test reaches 91 and 82% for 2022 proteins, 89 and 75% for 2161 proteins, and 85 and 73% for 2319 proteins, respectively. Furthermore, the predicting rate was tested by the three independent testing datasets containing 2240 proteins, 2513 proteins, and 2591 proteins. The correct prediction rates reach 82, 75, and 73% for 2240 proteins, 2513 proteins, and 2591 proteins, respectively.