Li Feng-Min, Wang Xiao-Qian
College of Science, Inner Mongolia Agricultural University, Hohhot, 010018, China.
Sci Rep. 2016 Sep 27;6:33910. doi: 10.1038/srep33910.
Cancer is one of the main causes of threats to human life. Identification of anticancer peptides is important for developing effective anticancer drugs. In this paper, we developed an improved predictor to identify the anticancer peptides. The amino acid composition (AAC), the average chemical shifts (acACS) and the reduced amino acid composition (RAAC) were selected to predict the anticancer peptides by using the support vector machine (SVM). The overall prediction accuracy reaches to 93.61% in jackknife test. The results indicated that the combined parameter was helpful to the prediction for anticancer peptides.
癌症是威胁人类生命的主要原因之一。鉴定抗癌肽对于开发有效的抗癌药物很重要。在本文中，我们开发了一种改进的预测器来鉴定抗癌肽。通过支持向量机（SVM）选择氨基酸组成（AAC）、平均化学位移（acACS）和简化氨基酸组成（RAAC）来预测抗癌肽。留一法检验中总体预测准确率达到93.61%。结果表明，组合参数有助于抗癌肽的预测。
