Prediction of Membrane Protein Types by Using Support Vector Machine Based on Composite Vector

Ting Wang, Xiuzhen Hu
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Abstract

By using of the composite vector with increment of diversity and scoring function to express the information of sequence, a support vector machine (SVM) algorithm for predicting the eight types of membrane proteins is proposed. The overall jackknife success rate is 91.81% what is higher than other results. In order to evaluate the predictive method, the six types of membrane proteins are predicted by using our method. The better results are obtained.
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基于复合向量的支持向量机预测膜蛋白类型
利用多样性增量复合向量和评分函数表达序列信息,提出了一种预测8种膜蛋白的支持向量机算法。整体叠刀成功率为91.81%,高于其他结果。为了对预测方法进行评价,对6种膜蛋白进行了预测。取得了较好的效果。
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