方正



【方正】个人简历
生日:1983.7
籍贯:四川自贡市
毕业院校:四川大学化学学院
E-Mail:
通信地址:
兴趣爱好:
打球,摄影,平面设计
主要科研方向

蛋白质单点突变对其稳定性的影响





主要科研课题

5


近期发表文章
  • 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.



  • 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.