基于监督学习的蛋白质络合物抽取方法

Abstract

Abstract: Protein complexes are important for understanding principles of cellular organization and function.Predicting protein complexes fromprotein-protein interaction (PPI) networks is of great significance.Previous methods for complex prediction are usually based on topological structure withoutconsidering the structure of complexes.In this paper,a supervised learning method is used to solve this problem.The features are constructed by multipleinformation of complex and the model obtained by the supervised method is usedinthe algorithm of complexes detection.The experimental results show that the method is an effective approach to predict protein complex from proteininteraction network.

Key words: protein interaction network, protein complex, supervised learning

CLC Number:

TP391.3

TANG Nan, YANG Zhi-hao, WU Jia-jin, WANG Yan-hua, LIN Hong-fei. Method of Predicting Protein Complex Based on Supervised Learning[J].Journal of Guangxi Normal University(Natural Science Edition), 2011, 29(2): 174-179.

References

[1] BADER G,HOUGE C.An automated method for finding molecular complexes in large protein interaction networks[J].BMC Bioinformatics,2003,4:2.
[2] WU Min,LI Xiao-li,KWOH C K,et al.A core-attachment based methodto detect protein complexes in PPI networks[J].BMC Bioinformatics,2009,10:169.
[3] 夏佞,林鸿飞,杨志豪.基于扩展语义特征机器学习消歧的基因提及标准化[J].广西师范大学学报:自然科学版,2010,28(3):144-147.
[4] CHEN Lei,SHI Xiao-he,KONG Xiang-yin.Identifying protein complexes using hybrid properties[J].Proteome,2009,8(11):5212-5218.
[5] LUBOVAC Z,GAMALIELSSON J,OLSSON B.Combining functional and topological properties to identify core modules in protein interaction networks[J].Proteins,2006,64:948-959.
[6] XENARIOS I,SALWINSKI L,DUAN X,etal.DIP,the database of interacting proteins:a research tool for studying cellular networks of protein interactions[J].Nucleic Acids Research,2002,30:303-305.
[7] DWIGHT S,HARRIS M,DOLINSKI K,et al.Saccharomyces genome database provides secondary gene annotation using the gene ontology[J].Nucleic Acids Research,2002,30:69-72.
[8] VLADIMIR N V.The nature of statistical learning theory[M].2nd ed.NewYork:Spring,1999:171-180.
[9] COSSOCK D,ZHANG Tong.Subset ranking using regression[C]//Proceedings of Conference on Learning Theory (COLT).Berlin:Spring,2006:605-619.
[10] TOMITA E,TANAKA A,TAKAHASHI H.The worst-case time complexity forgenerating all maximal cliques and computational experiments[J].Theor ComputSci,2006,363:28-42.
[11] BROHEE S,HELDEN J V.Evaluation of clustering algorithms for protein-protein interaction networks[J].BMC Bioinformatics,2006,7:488.
[12] LIU Gui-mei,WONG L,CHUA H N.Complex discovery from weighted PPInetworks[J].Bioinformatics,2009,25(15):1891-1897.

Metrics

Viewed

Full text

Abstract

Cited

Shared

Discussed

Comments

Recommended 0

No Suggested Reading articles found!

Method of Predicting Protein Complex Based on Supervised Learning

Abstract

Cite this article

share this article

References

Related Articles 3

Metrics

Comments

Recommended 0

[1]	LU Guang-quan, XIE Yang-cai, LIU Xing, ZHANG Shi-chao. An Improvement Semi-supervised Learning Based on KNN Classification [J]. Journal of Guangxi Normal University(Natural Science Edition), 2012, 30(1): 45-49.
[2]	HUANG Tian-qiang, LI Kai, ZHENG Zhi. Algorithm of Supervised Learning on Outlier Manifold [J]. Journal of Guangxi Normal University(Natural Science Edition), 2011, 29(3): 131-135.
[3]	WU Chao, ZHONG Yi-wen. Protein Function Prediction Using Ant Colony Optimization Algorithm [J]. Journal of Guangxi Normal University(Natural Science Edition), 2011, 29(3): 136-141.