期刊
AMINO ACIDS
卷 42, 期 6, 页码 2195-2205出版社
SPRINGER WIEN
DOI: 10.1007/s00726-011-0959-2
关键词
Membrane protein; Transmembrane helix prediction; Non-parametric model; Self-organizing map; Probabilistic neural network; SOMPNN
资金
- National Natural Science Foundation of China [60704047]
- Priority Academic Program Development of Jiangsu Higher Education Institutions
- Foundation for the Author of National Excellent Doctoral Dissertation of PR China [201048]
- Shanghai Pujiang Program
- Shanghai Municipal Education Commission [10ZZ17]
Accurately predicting the transmembrane helices (TMH) in a helical membrane protein is an important but challenging task. Recent researches have demonstrated that statistics-based methods are promising routes to improve the TMH prediction accuracy. However, most of existing TMH predictors are parametric models and they have to make assumptions of several or even hundreds of adjustable parameters based on the underlying probability distribution, which is difficult when no a priori knowledge is available. Besides the performances of these parametric predictors significantly depend on the estimated parameters, some of them need to exploit the entire training dataset in the prediction stage, which will lead to low prediction efficiency and this problem will become even worse when dealing with large-scale dataset. In this paper, we propose a novel SOMPNN model for prediction of TMH that features by minimal parameter assumptions requirement and high computational efficiency. In the SOMPNN model, a self-organizing map (SOM) is used to adaptively learn the helices distribution knowledge hidden in the training data, and then a probabilistic neural network (PNN) is adopted to predict TMH segments based on the knowledge learned by SOM. Experimental results on two benchmark datasets show that the proposed SOMPNN outperforms most existing popular TMH predictors and is promising to be extended to deal with other complicated biological problems. The datasets and the source codes of SOMPNN are available at http://www.csbio.sjtu.edu.cn/bioinf/SOMPNN/.
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