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作 者:DengXinyang XuPeida DengYong
机构地区:[1]SchoolofComputerandInformationScience,SouthwestUniversity,Chongqing400715,China [2]SchoolofElectronicsandInformationTechnology,ShanghaiJiaotongUniversity,Shanghai200240,China
出 处:《Journal of Electronics(China)》2012年第1期142-147,共6页电子科学学刊(英文版)
基 金:Supported by the National Natural Science Foundation of China (No. 60874105, 61174022);the Program for New Century Excellent Talents in University (No. NCET-08-0345);the Chongqing Natural Science Foundation (No. CSCT, 2010BA2003)
摘 要:Transmembrane proteins are some special and important proteins in cells. Because of their importance and specificity, the prediction of the transmembrane regions has very important theoretical and practical significance. At present, the prediction methods are mainly based on the physicochemical property and statistic analysis of amino acids. However, these methods are suitable for some environments but inapplicable for other environments. In this paper, the multi-sources information fusion theory has been introduced to predict the transmembrane regions. The proposed method is test on a data set of transmembrane proteins. The results show that the proposed method has the ability of predicting the transmembrane regions as a good performance and powerful tool.Transmembrane proteins are some special and important proteins in cells. Because of their importance and specificity, the prediction of the transmembrane regions has very important theoretical and practical significance. At present, the prediction methods are mainly based on the physicochemical property and statistic analysis of amino acids. However, these methods are suitable for some envi- ronments but inapplicable for other environments. In this paper, the multi-sources information fusion theory has been introduced to predict the transmembrane regions. The proposed method is test on a data set of transmembrane proteins. The results show that the proposed method has the ability of predicting the transmembrane regions as a good performance and powerful tool.
关 键 词:Transmembrane regions PREDICTION Dempster-Shafer theory of evidence PROTEINS
分 类 号:TP391[自动化与计算机技术—计算机应用技术]
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