Transmembrane protein prediction using N-gram and random forests

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	Transmembrane protein prediction using N-gram and random forests
	Li, Jinjin ; Xu, Lei ; Yang, Chenhui ; Jiang, Yi ; Yang CH(杨晨晖) ; Jiang Y(江弋)
刊名	http://dx.doi.org/10.1166/jctn.2014.3670
	2014
关键词	Algorithms Amino acids Artificial intelligence Bioinformatics Biological membranes Computer aided diagnosis Computer aided language translation Decision trees Feature extraction Learning systems Molecular biology Motion compensation
英文摘要	With recent development of proteomics, the importance of transmembrane proteins has been widely acknowledged. Previous bioinformatics studies have mainly focused on the classification of membrane proteins and ignored the important role of transmembrane proteins. In this study, we integrated the preceding order of amino acids into a machine learning approach based on a revamped N-gram model to predict transmembrane proteins using only protein sequence information. The framework consists of two steps: The N-gram model revamped for processing protein sequences was used as a feature extraction algorithm; then, we compared the performance of the popular classifiers logistic regression, Random Forests, support vector machine, and K-nearest neighbor using the N-gram model. N-gram combined with the Random Forests classifier obtained the highest accuracy at 95.6%, which is higher than other methods. The finding can help future studies on the structure and function of transmembrane proteins, drug design, and the classification of membrane proteins. In addition, a publicly accessible web server and software was established. Copyright ? 2014 American Scientific Publishers. All rights reserved.
语种	英语
出版者	American Scientific Publishers
内容类型	期刊论文
源URL	[http://dspace.xmu.edu.cn/handle/2288/92946]
专题	信息技术－已发表论文
推荐引用方式 GB/T 7714	Li, Jinjin,Xu, Lei,Yang, Chenhui,et al. Transmembrane protein prediction using N-gram and random forests[J]. http://dx.doi.org/10.1166/jctn.2014.3670,2014.
APA	Li, Jinjin,Xu, Lei,Yang, Chenhui,Jiang, Yi,杨晨晖,&江弋.(2014).Transmembrane protein prediction using N-gram and random forests.http://dx.doi.org/10.1166/jctn.2014.3670.
MLA	Li, Jinjin,et al."Transmembrane protein prediction using N-gram and random forests".http://dx.doi.org/10.1166/jctn.2014.3670 (2014).