谭福元



【谭福元】个人简历
生日:
籍贯:重庆市云阳县
毕业院校:
E-Mail:scuctfy@163.com
通信地址:
兴趣爱好:
骑车,旅游
主要科研方向

质谱数据挖掘






近期发表文章
  • Effects of neighboring sequence environment in predicting cleavage sites of signal peptides

  • Peptides,2008 Sep;29(9):1498-504. Epub 2008 Apr 26

    Yizhou Li  , Zhining Wen  , Cuisong Zhou  , Fuyuan Tan  , Menglong Li

    Abstract:  

    Signal peptide has a pivotal role in the translocation of secretory protein. Some models have been designed to predict its cleavage site. It is reported that the cleavage site has relationship with the neighboring sequence environment, i.e., hydrophobic core h-region, and the specific patterns in c-region. In some studies, this finding does facilitate the prediction of cleavage site. However, in these models, sequence environment information is merely taken account of as model inputs and no detailed investigation into its effect on the prediction of cleavage site has been made. In this work, we analyze the constraint on cleave site placed by the hydrophobic core of signal peptide and then use it to improve the performance of the signal peptide cleavage site prediction. Our model is designed as follows: firstly, a sliding window is used to scan sample and artificial neural network (ANN) is employed to give cleavage site/non-cleavage site scores. Then, based on an estimated hydrophobic h-region a correcting function is proposed to improve the prediction result, in which the sequence environment is taken into account. A trend of cleavage site is indicated by our analysis for each position, which is consistent with experimental findings. Through this correcting step, the improvement of prediction accuracy is over 7%. It therefore demonstrates the neighboring sequence environment is helpful for determination of cleavage site. Program written in Matlab can be downloaded from http://www.scucic.cn/combined model/source code.html.



  • Prediction of mitochondrial proteins based on genetic algorithm – partial least squares and support vector machine

  • Amino Acids,Volume 33, Number 4, 669-675,

    Fuyuan Tan  , XiaoYu Feng  , Fang Zheng  , Menglong Li*  , Yanzhi Guo  , Lin JIANG 

    Abstract:  

    Mitochondria are essential cell organelles of eukaryotes. Hence, it is vitally important to develop an automated and reliable method for timely identification of novel mitochondrial proteins. In this study, mitochondrial proteins were encoded by dipeptide composition technology; then, the genetic algorithm-partial least square (GA-PLS) method was used to evaluate the dipeptide composition elements which are more important in recognizing mitochondrial proteins; further, these selected dipeptide composition elements were applied to support vector machine (SVM)-based classifiers to predict the mitochondrial proteins. All the models were trained and validated by the jackknife cross-validation test. The prediction accuracy is 85%, suggesting that it performs reasonably well in predicting the mitochondrial proteins. Our results strongly imply that not all the dipeptide compositions are informative and indispensable for predicting proteins. The source code of MATLAB and the dataset are available on request under liml@scu.edu.cn.



  • Local Sequence Information-based Support Vector Machine to Classify Voltage-gated Potassium Channels

  • Acta Biochimica et Biophysica Sinica,(2006)38(6): 363-371.

    LiXia Liu  , Menglong Li*  , Fuyuan Tan  , MinChun Lu  , Ke Long Wang  , Yanzhi Guo  , Zhining Wen  , Lin JIANG 

    Abstract:  

    In our previous work, we developed a computational tool, PreK-ClassK-ClassKv, to predict and classify potassium (K+) channels. For K+ channel prediction (PreK) and classification at family level (ClassK), this method performs well. However, it does not perform so well in classifying voltage-gated potassium (Kv) channels (ClassKv). In this paper, a new method based on the local sequence information of Kv channels is introduced to classify Kv channels. Six transmembrane domains of a Kv channel protein are used to define a protein, and the dipeptide composition technique is used to transform an amino acid sequence to a numerical sequence. A Kv channel protein is represented by a vector with 2000 elements, and a support vector machine algorithm is applied to classify Kv channels. This method shows good performance with averages of total accuracy (Acc), sensitivity (SE), specificity (SP), reliability (R) and Matthews correlation coefficient (MCC) of 98.0%, 89.9%, 100%, 0.95 and 0.94 respectively. The results indicate that the local sequence information-based method is better than the global sequence information-based method to classify Kv channels.



  • 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.