刁元波

博士、讲师

【刁元波】个人简历
生日:
籍贯:四川省宜宾市
毕业院校:四川大学
E-Mail:
通信地址:四川大学化学学院
兴趣爱好:

主要科研方向

生物信息学






近期发表文章
  • The community structure of human cellular signaling network

  • Journal of Theoretical Biology,2007 Aug 21;247(4):608-615

    YuanBo DIAO  , Menglong Li*  , Fang Zheng 

    Abstract:  

    Living cell is highly responsive to specific chemicals in its environment, such as hormones and molecules in food or aromas. The reason is ascribed to the existence of widespread and diverse signal transduction pathways, between which crosstalks usually exist, thus constitute a complex signaling network. Evidently, knowledge of topology characteristic of this network could contribute a lot to the understanding of diverse cellular behaviors and life phenomena thus come into being. In this presentation, signal transduction data is extracted from KEGG to construct a cellular signaling network of Homo sapiens, which has 931 nodes and 6798 links in total. Computing the degree distribution, we find it is not a random network, but a scale-free network following a power-law of P(K) approximately K(-gamma), with gamma approximately equal to 2.2. Among three graph partition algorithms, the Guimera's simulated annealing method is chosen to study the details of topology structure and other properties of this cellular signaling network, as it shows the best performance. To reveal the underlying biological implications, further investigation is conducted on ad hoc community and sketch map of individual community is drawn accordingly. The involved experiment data can be found in the supplementary material.



  • Investigation of the proteins folding rates and their properties of amino acid networks

  • Chemometrics and Intelligent Laboratory Systems,(2010) 123–129

    Yaping Fang  , DaiChuan Ma  , Menglong Li*  , Zhining Wen  , YuanBo DIAO 

    Abstract:  

    The mechanism of protein folding is an important problem in molecular biology. It is usually thought that protein folding is a complex system process related to the entire molecule. In this article, we have investigated 78 structures of folding proteins in native state, from complex networks perspective, to understand the role of topological parameters in proteins folding kinetics. The 31 parameters were calculated based on the amino acid networks of the folding proteins. The relationship between those parameters and protein folding rates has been systematically analyzed. Our results show that the significant parameters between two-state and multi-state folding proteins correlate well with the folding rates of proteins. It is also found that classifying the proteins into different classes can improve the correlation coefficient from 0.926 to 0.983 between the parameters and folding rates of two- and multistate proteins, respectively. Genetic Algorithms–Multiple Linear Regression (GA–MLR) was adopted to select the best subset parameters from the whole 31 parameters to construct the MLR model to avoid overfitting. Ourmethods showa correlation coefficient of 0.921 for the all folding proteins based on the classification of the folding proteins. The results indicate that the general topological parameters of the amino acids networks of the folding proteins can effectively represent the structural and functional properties, such as the rates of folding.



  • Prediction of mitochondrial proteins using discrete wavelet transform

  • The protein Journal,2006, 25(4):241-249

    Lin JIANG  , Menglong Li*  , Zhining Wen  , Ke Long Wang  , YuanBo DIAO 

    Abstract:  

    A new method was proposed for prediction of mitochondrial proteins by the discrete wavelet transform, based on the sequence–scale similarity measurement. This sequence–scale similarity, revealing more information than other conventional methods, does not rely on subcellular location information and can directly predict protein sequences with different length. In our experiments, 499 mitochondrial protein sequences, constituting a mitochondria database, were used as training dataset, and 681 non-mitochondrial protein sequences were tested. The system can predict these sequences with sensitivity, specificity, accuracy and MCC of 50.30%, 95.74%, 76.53% and 0.54, respectively. Source code of the new program is available on request from the authors.