|Table of Contents|

Predicting GPCR-drug interactions with multi-view featurecombination and random forest

《南京理工大学学报》(自然科学版)[ISSN:1005-9830/CN:32-1397/N]

Issue:
2016年01期
Page:
1-
Research Field:
Publishing date:

Info

Title:
Predicting GPCR-drug interactions with multi-view featurecombination and random forest
Author(s):
Liu Guanghui 12Hu Jun 1Yu Dongjun 1
1.School of Computer Science and Engineering,Nanjing University of Science and Technology,Nanjing 210094,China; 2.School of Information Engineering,Nanjing University of Finance and Economics,Nanjing 210046,China
Keywords:
coupled recptors G-protein-coupled receptors drugs multi-view features amino acid composition sequence features molecular fingerprint random forest
PACS:
TP391.4
DOI:
-
Abstract:
In order to improve the accuracy of predicting the interactions between G-protein-coupled receptors(GPCR)and drugs,this paper develops a novel method based on multi-view feature combination and random forest for GPCR-Drug interactions prediction with high performance.In the method,GPCR features from amino acid composition and protein evolution views and drug feature from molecular fingerprint are extracted; the feature of every GPCR-Drug pair can be formulated by serially combining the multi-view features of GPCRs and drugs; the GPCR-Drug prediction model is constructed with the random forest algorithm under the developed feature representation.Stringent experiments on benchmark datasets over both cross-validation and independent validation tests demonstrate the feasibility and efficacy of the proposed method.

References:

[1] Kroeze W K,Sheffler D J,Roth B L.G-protein-coupled receptors at a glance[J].Journal of Cell Science,2003,116(24):4867-4869.
[2]Agrawal N J,Helk B,Trout B L.A computational tool to predict the evolutionarily conserved protein-protein interaction hot-spot residues from the structure of the unbound protein[J].FEBS Letters,2014,588(2):326-333.
[3]Chou Kuochen.Prediction of G-protein-coupled receptor classes[J].Journal of Proteome Research,2005,4(4):1413-1418.
[4]Karnik S S,Gogonea C,Patil S,et al.Activation of G-protein-coupled receptors:a common molecular mechanism[J].Trends in Endocrinology & Metabolism,2003,14(9):431-437.
[5]Albert B,Johnson A,Lewis J,et al.Molecular biology of the cell[M].4th ed.New York:Garland Science Press,2002.
[6]张君,金亚,叶燕锐,等.G蛋白偶联受体配体结合分析技术[J].药物分析杂志,2015,35(1):1-7.

Zhang Jun,Jin Ya,Ye Yanrui,et al.Technologies in G-protein-coupled receptor-ligand binding assays[J].Chinese Journal of Pharmaceutical Analysis,2015,35(1):1-7.
[7]Yamanishi Y,Araki M,Gutteridge A,et al.Prediction of drug-target interaction networks from the integration of chemical and genomic spaces[J].Bioinformatics,2008,24(13):i232-i240.
[8]He Zhisong,Zhang Jian,Shi Xiaohe,et al.Predicting drug-target interaction networks based on functional groups and biological features[J].PloS One,2010,5(3):e9603.
[9]Xiao Xuan,Min Jianliang,Wang Pu,et al.iGPCR-Drug:A web server for predicting interaction between GPCRs and drugs in cellular networking[J].PloS One,2013,8(8):e72234.
[10]Kanehisa M,Goto S,Hattori M,et al.From genomics to chemical genomics:new developments in KEGG[J].Nucleic Acids Research,2006,34(suppl 1):D354-D357.
[11]Chou Kuochen.Prediction of protein cellular attributes using pseudo-amino acid composition[J].Proteins:Structure,Function,and Bioinformatics,2001,43(3):246-255.
[12]Yu Dongjun,Hu Jun,Wu Xiaowei,et al.Learning protein multi-view features in complex space[J].Amino Acids,2013,44(5):1365-1379.
[13]Sch?ffer A A,Aravind L,Madden T L,et al.Improving the accuracy of PSI-BLAST protein database searches with composition-based statistics and other refinements[J].Nucleic Acids Research,2001,29(14):2994-3005.
[14]Chou Kuochen,Shen Hongbin.MemType-2L:a web server for predicting membrane proteins and their types by incorporating evolution information through Pse-PSSM[J].Biochemical and Biophysical Research Communications,2007,360(2):339-345.
[15]O’Boyle N M,Banck M,James C A,et al.Open Babel:An open chemical toolbox[J].Journal of Cheminformatics,2011,3(1):1-14.
[16]Butina D.Unsupervised data base clustering based on daylight’s fingerprint and Tanimoto similarity:A fast and automated way to cluster small and large data sets[J].Journal of Chemical Information and Computer Sciences,1999,39(4):747-750.
[17]Dou Yangchao,Wang Jun,Yang Jialiang,et al.L1pred:a sequence-based prediction tool for catalytic residues in enzymes with the L1-logreg classifier[J].PloS One,2012,7(4):e35666.
[18]Villasenor J D,Belzer B,Liao J.Wavelet filter evaluation for image compression[J].Image Processing,IEEE Transactions on,1995,4(8):1053-1060.
[19]冯凯,应展烽,吴军基,等.基于小波包变换和峰式马尔科夫链的风速短期预测[J].南京理工大学学报,2014,38(5):639-643.
Feng Kai,Ying Zhanfeng,Wu Junji,et al.Short-term wind speed forecast based on wavelet packet decomposition and peak-type Markov chain[J].Journal of Nanjing University of Science and Technology,2014,38(5):639-643.
[20]Breiman L.Random forests[J].Machine Learning,2001,45(1):5-32.
[21]黄衍,查伟雄.随机森林与支持向量机分类性能比较[J].软件,2012,33(6):107-110.
Huang Yan,Zha Weixiong.Comparison on classification performance between random forests and support vector machine[J].Software,2012,33(6):107-110.
[22]Yu Dongjun,Hu Jun,Huang Yan,et al.Target ATP site:A template-free method for ATP-binding sites prediction with residue evolution image sparse representation and classifier ensemble[J].Journal of Computational Chemistry,2013,34(11):974-985.
[23]魏志森,杨静宇,於东军.基于加权PSSM 直方图和随机森林集成的蛋白质交互作用位点预测[J].南京理工大学学报,2015,39(4):379-385.
Wei Zhisen,Yang Jingyu,Yu Dongjun.Protein-protein interaction sites prediction based on weighted PSSM histogram and random forests ensemble[J].Journal of Nanjing University of Science and Technology,2015,39(4):379-385.
[24]汤永利,李伟杰,于金霞,等.基于改进DS证据理论的网络安全态势评估方法[J].南京理工大学学报,2015,39(4):405-411.
Tang Yongli,Li Weijie,Yu Jinxia,et al.Network secur-ity situational assessment method based on improved D-S evidence theory[J].Journal of Nanjing University of Science and Technology,2015,39(4):405-411.

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Last Update: 2016-02-29