余乐正



【余乐正】个人简历
生日:1984年7月
籍贯: 四川省乐山市
毕业院校:
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兴趣爱好:
打乒乓球,听音乐等
主要科研方向

生物信息学方向 分泌蛋白的分类预测






近期发表文章
  • SecretP: Identifying bacterial secreted proteins by fusing new features into Chou’s pseudo-amino acid composition

  • Journal of Theoretical Biology,(2010)1–6

    LeZheng Yu  , Yanzhi Guo  , Yizhou Li  , Menglong Li*  , Jiesi Luo  , WenJia Xiong  , WenLi Qin 

    Abstract:  

    Protein secretion plays an important role in bacterial lifestyles. Secreted proteins are crucial for bacterial pathogenesis by making bacteria interact with their environments, particularly delivering pathogenic and symbiotic bacteria into their eukaryotic hosts. Therefore, identification of bacterial secreted proteins becomes an important process for the study of various diseases and the corresponding drugs. In this paper, fusing several new features into Chou’s pseudo-amino acid composition (PseAAC), two support vector machine (SVM)-based ternary classifiers are developed to predict secreted proteins of Gram-negative and Gram-positive bacteria. For the two types of bacteria, the high accuracy of 94.03% and 94.36% are obtained in distinguishing classically secreted, non-classically secreted and non-secreted proteins by our method. In order to compare the practical ability of our method in identifying bacterial secreted proteins with those of six published methods, proteins in Escherichia coli and Bacillus subtilis are collected to construct the test sets of Gram-negative and Gram-positive bacteria, and the prediction results of our method are comparable to those of existing methods. When performed on two public independent data sets for predicting NCSPs, it also yields satisfactory results for Gram-negative bacterial proteins. The prediction server SecretP can be accessed at http://cic.scu.edu.cn/bioinformatics/secretPV2/index.htm.



  • Using the augmented Chou‘s pseudo amino acid composition for predicting protein submitochondria locations based on auto covariance approach.

  • Journal of Theoretical Biology,(2009)366–372

    Yuhong Zeng  , Yanzhi Guo  , Rongquan Xiao  , Li Yang  , LeZheng Yu  , Menglong Li

    Abstract:  

    The submitochondria location of a mitochondrial protein is very important for further understanding the structure and function of this protein. Hence, it is of great practical significance to develop an automated and reliable method for timely identifying the submitochondria locations of novel mitochondrial proteins. In this study, a sequence-based algorithm combining the augmented Chou's pseudo amino acid composition (Chou's PseAA) based on auto covariance (AC) is developed to predict protein submitochondria locations and membrane protein types in mitochondria inner membrane. The model fully considers the sequence-order effects between residues a certain distance apart in the sequence by AC combined with eight representative descriptors for both common proteins and membrane proteins. As a result of jackknife cross-validation tests, the method for submitochondria location prediction yields the accuracies of 91.8%, 96.4% and 66.1% for inner membrane, matrix, and outer membrane, respectively. The total accuracy is 89.7%. When predicting membrane protein types in mitochondria inner membrane, the method achieves the prediction performance with the accuracies of 98.4%, 64.3% and 86.7% for multi-pass inner membrane, single-pass inner membrane, and matrix side inner membrane, where the total accuracy is 93.6%. The overall performance of our method is better than the achievements of the previous studies. So our method can be an effective supplementary tool for future proteomics studies. The prediction software and all data sets used in this article are freely available at http://chemlab.scu.edu.cn/Predict_subMITO/index.htm.