期刊
JOURNAL OF MEMBRANE BIOLOGY
卷 248, 期 6, 页码 1005-1014出版社
SPRINGER
DOI: 10.1007/s00232-015-9811-z
关键词
Antifreeze protein prediction; Multi-view protein features; Support vector machine; Machine learning
资金
- National Natural Science Foundation of China [61373062, 61222306, 61202134, 61233011]
- Natural Science Foundation of Jiangsu [BK20141403]
- Jiangsu Postdoctoral Science Foundation [1201027C]
- Jiangsu University Graduate Research and Innovation Project [KYZZ_0123]
- China Postdoctoral Science Foundation [2013M530260, 2014T70526]
- The Six Top Talents of Jiangsu Province [2013-XXRJ-022]
- Fundamental Research Funds for the Central Universities [30920130111010]
Antifreeze proteins (AFPs) are indispensable for living organisms to survive in an extremely cold environment and have a variety of potential biotechnological applications. The accurate prediction of antifreeze proteins has become an important issue and is urgently needed. Although considerable progress has been made, AFP prediction is still a challenging problem due to the diversity of species. In this study, we proposed a new sequence-based AFP predictor, called TargetFreeze. TargetFreeze utilizes an enhanced feature representation method that weightedly combines multiple protein features and takes the powerful support vector machine as the prediction engine. Computer experiments on benchmark datasets demonstrate the superiority of the proposed TargetFreeze over most recently released AFP predictors. We also implemented a user-friendly web server, which is openly accessible for academic use and is available at http://csbio.njust.edu.cn/bioinf/TargetFreeze. TargetFreeze supplements existing AFP predictors and will have potential applications in AFP-related biotechnology fields.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据