.png){kind=icon}
返回
Protein structural classification based on pseudo amino acid composition using SVM classifier
.png){kind=icon}
PRE
AI
DOI：10.1016/j.bbe.2013.03.002.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
This paper deals with a structural classification by the aid of support vector machine (SVM) classifier. Amino acid composition (AAC) and pseudo amino acid composition (PseAA) features were applied with different variants. Additionally the feature reflecting the length of protein chain was taken into consideration. The SVM classifier was compared to minimal-length classifiers with respect to the AAC features. The best model of SVM classifier was chosen using grid method on the basis of cross-validation (CV) as criterion. The best model of SVM classifier is evaluated with respect to proper evaluation rates. The SCOP database and the ASTRAL tool were a source of non-homologous data to avoid the redundancy and to ensure a maximal amount of available data. (C) 2013 Published by Elsevier Urban & Partner Sp. z o.o. on behalf of Nalecz Institute of Biocybemetics and Biomedical Engineering.
Keyword:
Pseudo amino acid composition
Support vector machine
Minimal-distance methods
Protein structural class
SCOP database
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
6.6
论文数:
944
被引数:
3.3K
机构
引用论文
.png){kind=icon}
查看更多内容