肖嘉敏



【肖嘉敏】个人简历
生日:
籍贯:湖南株洲
毕业院校:邵阳学院
E-Mail:
通信地址:
兴趣爱好:

主要科研方向

1. MicroRNA及其靶标基因的预测

2. Working on the development of classifiers for microRNA and its target gene prediction based on machine learning method.






近期发表文章
  • Identification of RNA-binding sites in proteins by integrating various sequence information

  • Amino Acids,s00726-010-0639-7

    CuiCui Wang  , Yaping Fang  , JiaMin Xiao  , Menglong Li

    Abstract:  

    RNA–protein interactions play a pivotal role in various biological processes, such as mRNA processing, protein synthesis, assembly, and function of ribosome. In this work, we have introduced a computational method for predicting RNA-binding sites in proteins based on support vector machines by using a variety of features from amino acid sequence information including position-specific scoring matrix (PSSM) profiles, physicochemical properties and predicted solvent accessibility. Considering the influence of the surrounding residues of an amino acid and the dependency effect from the neighboring amino acids, a sliding window and a smoothing window are used to encode the PSSM profiles. The outer fivefold cross-validation method is evaluated on the data set of 77 RNA-binding proteins (RBP77). It achieves an overall accuracy of 88.66% with the Matthew’s correlation coefficient (MCC) of 0.69. Furthermore, an independent data set of 39 RNA-binding proteins (RBP39) is employed to further evaluate the performance and achieves an overall accuracy of 82.36% with the MCC of 0.44. The result shows that our method has good generalization abilities in predicting RNA-binding sites for novel proteins. Compared with other previous methods, our method performs well on the same data set. The prediction results suggest that the used features are effective in predicting RNA-binding sites in proteins. The code and all data sets used in this article are freely available at http://cic.scu.edu.cn/bioinformatics/Predict_RBP.rar.



  • In silico method for systematic analysis of feature importance in microRNA-mRNA interactions

  • BMC Bioinformatics,2009, 10:427

    JiaMin Xiao  , Yizhou Li  , Ke Long Wang  , Zhining Wen  , Menglong Li*  , LiFang Zhang  , guangxuan Min 

    Abstract:  

    Background: MicroRNA (miRNA), which is short non-coding RNA, plays a pivotal role in the regulation of many biological processes and affects the stability and/or translation of mRNA. Recently, machine learning algorithms were developed to predict potential miRNA targets. Most of these methods are robust but are not sensitive to redundant or irrelevant features. Despite their good performance, the relative importance of each feature is still unclear. With increasing experimental data becoming available, research interest has shifted from higher prediction performance to uncovering the mechanism of microRNA-mRNA interactions.

    Results: Systematic analysis of sequence, structural and positional features was carried out for two different data sets. The dominant functional features were distinguished from uninformative features in single and hybrid feature sets. Models were developed using only statistically significant sequence, structural and positional features, resulting in area under the receiver operating curves (AUC) values of 0.919, 0.927 and 0.969 for one data set and of 0.926, 0.874 and 0.954 for another data set, respectively. Hybrid models were developed by combining various features and achieved AUC of 0.978 and 0.970 for two different data sets. Functional miRNA information is well reflected in these features, which are expected to be valuable in understanding the mechanism of microRNAmRNA interactions and in designing experiments.

    Conclusions: Differing from previous approaches, this study focused on systematic analysis of all types of features. Statistically significant features were identified and used to construct models that yield similar accuracy to previous studies in a shorter computation time.



  • Using auto covariance method for functional discrimination of membrane proteins based on evolution information

  • Amino Acids,(2010) 38:1497–1503

    Li Yang  , Yizhou Li  , Rongquan Xiao  , Yuhong Zeng  , JiaMin Xiao  , Fuyuan Tan  , Menghui Chen

    Abstract:  

    Membrane transporters are critical in living cells. Therefore, the discrimination of the types of membrane proteins based on their functions is of great importance both for helping genome annotation and providing a supplementary role to experimental researchers to gain insight into membrane proteins’ function. There are a lot of computational methods to facilitate the identification of the functional types of membrane proteins. However, in these methods, the local sequence environment was not integrated into the constructed model. In this study, we described a new strategy to predict the functional types of membrane proteins using a model based on auto covariance and position-specific scoring matrix. The novelty of the presented approach is considering the distribution of different positions of functional conservation sites in protein sequences. Thereby, this model adequately takes into account the long-range correlation between such sites during sequential evolution. Fivefold cross-validation test shows that this method greatly improves the prediction
    accuracy and achieves an acceptable prediction accuracy of 87.51%. The result indicates that the current approach might be an effective tool for predicting the functional
    types of membrane proteins only using the primary sequences. The code and dataset used in this article are freely available at http://cic.scu.edu.cn/bioinformatics/
    predict_membrane.zip.