基于支持向量机的癌细胞经典分泌蛋白与非经典分泌蛋白识别研究

基于支持向量机算法，本文提出了一种能快速准确区分癌细胞经典分泌蛋白与非经典分泌蛋白的方法.通过严格的特征筛选，氨基酸组成、位置特异性得分矩阵和信号肽组成了最优特征集.测试集检测结果表明，本方法对癌细胞经典分泌蛋白与非经典分泌蛋白具有较强的区分能力，可为寻找到不同种类癌症间通用的生物标志物提供理论参考.

A study on recognition of classically and non-classically secreted proteins from cancer cells based on support vector machine

Based on support vector machine (SVM) algorithm, a fast and accurate method is proposed to distinguish the classically and non-classically secreted proteins from cancer cells. By a strict feature selection, the optimal feature set is obtained which consists of amino acid composition (AAC), position specificity score matrix (PSSM) and signal peptide (SP). The test results show that our method has strong ability to distinguish the non-classically secreted proteins (NCSPs) from the classically secreted proteins (CSPs) of cancer cells, which may provide theoretical reference for finding common biomarkers among different kinds of cancers.