Collective prediction of protein functions from protein-protein interaction networks

BMC Bioinformatics

Volumn 15, Issue , 2014, Pages

  Wu, Qingyao ^a,b   Ye, Yunming ^a,b   Ng, Michael K ^c   Ho, Shen Shyang ^d   Shi, Ruichao ^a,b  

^a HARBIN INSTITUTE OF TECHNOLOGY   (China)

^b Shenzhen Key Laboratory of Internet Information Collaboration   (China)

^c HONG KONG BAPTIST UNIVERSITY   (Hong Kong)

^d NANYANG TECHNOLOGICAL UNIVERSITY   (Singapore)

Author keywords

[No Author keywords available]

Indexed keywords

BIOINFORMATICS; CHAINS; CLASSIFICATION (OF INFORMATION); FORECASTING; ITERATIVE METHODS; LEARNING ALGORITHMS; MARKOV PROCESSES;

AUTOMATED ASSIGNMENTS; COLLECTIVE CLASSIFICATIONS; COMPUTATIONAL BIOLOGY; IMPROVING PERFORMANCE; LABELED TRAINING DATA; PREDICTION OF PROTEIN FUNCTION; PROTEIN FUNCTION PREDICTION; PROTEIN-PROTEIN INTERACTION NETWORKS;

PROTEINS;

CLASSIFICATION ALGORITHM; CLASSIFIER; EXPERIMENTAL MODEL; LEARNING ALGORITHM; MARKOV CHAIN; NONHUMAN; PREDICTION; PROTEIN FUNCTION; PROTEIN PROTEIN INTERACTION; YEAST; ALGORITHM; ARTICLE; METHODOLOGY; PHYSIOLOGY; PROBABILITY; PROTEIN ANALYSIS;

PROTEIN;

ALGORITHMS; MARKOV CHAINS; PROTEIN INTERACTION MAPPING; PROTEINS;

EID: 84901268258     PISSN: None     EISSN: 14712105     Source Type: Journal    
DOI: 10.1186/1471-2105-15-S2-S9     Document Type: Article

References (33)

1. Eisenberg D, Marcotte EM, Xenarios I, Yeates TO: Protein function in the post-genomic era. Nature 2000, 405(6788):823-826.
2. Pandey G, Kumar V, Steinbach M, Meyers CL: Computational Approaches to Protein Function Prediction. Wiley-Interscience; 2012.
3. Clare A, King RD: Predicting gene function in saccharomyces cerevisiae. Bioinformatics 2003, 19(suppl 2):42-49.
4. Borgwardt KM, Ong CS, Schönauer S, Vishwanathan S, Smola AJ, Kriegel H-P: Protein function prediction via graph kernels. Bioinformatics 2005, 21(suppl 1):47-56.
5. Sleator RD, Walsh P: An overview of in silico protein function prediction. Archives of microbiology 2010, 192(3):151-155.
6. Altschul SF: Evaluating the statistical significance of multiple distinct local alignments. Theoretical and Computational Methods in Genome Research 1997, 1-14.
7. Sigrist CJ, Cerutti L, De Castro E, Langendijk-Genevaux PS, Bulliard V, Bairoch A, Hulo N: Prosite, a protein domain database for functional characterization and annotation. Nucleic acids research 2010, 38(suppl 1):161-166.
8. Finn RD, Mistry J, Schuster-Böckler B, Griffiths-Jones S, Hollich V, Lassmann T, Moxon S, Marshall M, Khanna A, Durbin R, et al: Pfam: clans, web tools and services. Nucleic acids research 2006, 34(suppl 1):247-251.
9. Sharan R, Ulitsky I, Shamir R: Network-based prediction of protein function. Molecular systems biology 2007, 3(1).
10. Chua HN, Sung WK, Wong L: Exploiting indirect neighbours and topological weight to predict protein function from protein-protein interactions. Bioinformatics 2006, 22(13):1623-1630.
11. Nabieva E, Jim K, Agarwal A, Chazelle B, Singh M: Whole-proteome prediction of protein function via graph-theoretic analysis of interaction maps. Bioinformatics 2005, 21(suppl 1):302-310.
12. Brun C, Chevenet F, Martin D, Wojcik J, Gueénoche A, Jacq B, et al: Functional classification of proteins for the prediction of cellular function from a protein-protein interaction network. Genome biology 2004, 5(1):6-6.
13. Adamcsek B, Palla G, Farkas IJ, Derényi I, Vicsek T: Cfinder: locating cliques and overlapping modules in biological networks. Bioinformatics 2006, 22(8):1021-1023.
14. McDowell L, Aha DW: Semi-supervised collective classification via hybrid label regularization. Proceedings of the 29th International Conference on Machine Learning 2012.
15. Neville J, Jensen D: Iterative classification in relational data. Proc AAAI- 2000 Workshop on Learning Statistical Models from Relational Data 2000, 13-20.
16. Jensen D, Neville J, Gallagher B: Why collective inference improves relational classification. Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining 2004, 593-598.
17. Macskassy SA, Provost F: Classification in networked data: A toolkit and a univariate case study. The Journal of Machine Learning Research 2007, 8:935-983.
18. Shi X, Li Y, Yu P: Collective prediction with latent graphs. Proceedings of the 20th ACM International Conference on Information and Knowledge Management 2011, 1127-1136.
19. McDowell L, Aha D: Semi-supervised collective classification via hybrid label regularization. 2012, 975-982, arXiv preprint arXiv:1206.6467.
20. Mcdowell LK, Gupta KM, Aha DW: Case-based collective classification. Proceedings of the 20th International FLAIRS Conference 2007, 399-404.
21. Macropol K, Can T, Singh A: Rrw: repeated random walks on genomescale protein networks for local cluster discovery. BMC bioinformatics 2009, 10(1):283.
22. Li X, Ng MK, Ye Y: Multicomm: Finding community structure in multidimensional networks. IEEE Transactions on Knowledge and Data Engineering 2013, 99(1).
23. Wu Q, Ng MK, Ye Y: Markov-miml: A markov chain-based multi-instance multi-label learning algorithm. Knowledge and Information Systems .
24. Ng MK, Wu Q, Ye Y: Co-transfer learning via joint transition probability graph based method. Proceedings of the 1st International Workshop on Cross Domain Knowledge Discovery in Web and Social Network Mining 2012, 1-9, ACM.
25. Tong H, Faloutsos C, Pan J-Y: Random walk with restart: fast solutions and applications. Knowledge and Information Systems 2008, 14(3):327-346.
26. Zelnik-manor L, Perona P: Self-tuning spectral clustering. Advances in Neural Information Processing Systems 2004, 1601-1608.
27. Haveliwala TH: Topic-sensitive pagerank: A context-sensitive ranking algorithm for web search. IEEE Transactions on Knowledge and Data Engineering 2003, 784-796.
28. Cheng J, Hatzis C, Hayashi H, Morishita S, Page D, Sese J: Kdd cup 2001 report. ACM SIGKDD Explorations Newsletter 2002, 3(2):47-64.
29. Kong X, Shi X, Yu PS: Multi-label collective classification. SIAM International Conference on Data Mining (SDM) 2011, 618-629.
30. Schapire RE, Singer Y: Boostexter: A boosting-based system for text categorization. Machine learning 2000, 39(2-3):135-168.
31. Zhou Z-H, Zhang M-L, Huang S-J, Li Y-F: Multi-instance multi-label learning. Artificial Intelligence 2012, 176(1):2291-2320.
32. Davis J, Goadrich M: The relationship between precision-recall and roc curves. Proceedings of the 23rd International Conference on Machine Learning 2006, 233-240.
33. Chang C-C, Lin C-J: Libsvm: a library for support vector machines. ACM Transactions on Intelligent Systems and Technology (TIST) 2011, 2(3):27.