A MapReduce based parallel SVM for large-scale predicting protein-protein interactions

Neurocomputing

Volumn 145, Issue , 2014, Pages 37-43

  You, Zhu Hong ^a   Yu, Jian Zhong ^a   Zhu, Lin ^b   Li, Shuai ^c   Wen, Zhen Kun ^a  

^a SHENZHEN UNIVERSITY   (China)

^b TONGJI UNIVERSITY   (China)

^c HONG KONG POLYTECHNIC UNIVERSITY   (Hong Kong)

Author keywords

Autocorrelation descriptor; MapReduce; Protein sequence; Protein protein interaction; Support vector machine

Indexed keywords

AUTOCORRELATION; FORECASTING; MULTIPROCESSING SYSTEMS; SUPPORT VECTOR MACHINES;

BIOCHEMICAL PROCESS; DESCRIPTORS; EXPERIMENTAL METHODS; EXPERIMENTAL TECHNIQUES; MAP-REDUCE; MAPREDUCE FRAMEWORKS; PROTEIN SEQUENCES; PROTEIN-PROTEIN INTERACTIONS;

PROTEINS;

ACCURACY; ARTICLE; AUTOMATION; COMPUTER PROGRAM; CONTROLLED STUDY; DATA ANALYSIS; LEARNING ALGORITHM; MAPREDUCE MODEL; MATHEMATICAL COMPUTING; MATHEMATICAL MODEL; PREDICTION; PRIORITY JOURNAL; PROCESS DEVELOPMENT; PROTEIN PROTEIN INTERACTION; SEQUENCE ANALYSIS; SUPPORT VECTOR MACHINE;

EID: 84906935098     PISSN: 09252312     EISSN: 18728286     Source Type: Journal    
DOI: 10.1016/j.neucom.2014.05.072     Document Type: Article

References (41)

1. Krogan N.J., Cagney G., Yu H.Y., et al. Global landscape of protein complexes in the yeast Saccharomyces cerevisiae. Nature 2006, 440:637-643.
2. Ito T., Chiba T., Ozawa R., et al. A comprehensive two-hybrid analysis to explore the yeast protein interactome. Proc. Natl. Acad. Sci. U.S.A 2001, 98:4569-4574.
3. Gavin A.C., Bosche M., et al. Functional organization of the yeast proteome by systematic analysis of protein complexes. Nature 2002, 415:141-147.
4. Ho Y., Gruhler A., Heilbut A., et al. Systematic identification of protein complexes in Saccharomyces cerevisiae by mass spectrometry. Nature 2002, 415:180-183.
5. Giot L., Bader J.S., Brouwer C., et al. A protein interaction map of Drosophila melanogaster. Science 2003, 302:1727-1736.
6. Yu H.Y., Braun P., et al. High-quality binary protein interaction map of the yeast interactome network. Science 2008, 322:104-110.
7. Li S.M., C.M., et al. A map of the interactome network of the metazoan C. elegans. Science 2004, 303:540-543.
8. Butland G., Peregrin-Alvarez J.M., Li J., et al. Interaction network containing conserved and essential protein complexes in Escherichia coli. Nature 2005, 433:531-537.
9. Ewing R.M., Chu P., Elisma F., et al. Large-scale mapping of human protein-protein interactions by mass spectrometry. Mol. Syst. Biol. 2007, 3.
10. Uetz P., Giot L., Cagney G., et al. A comprehensive analysis of protein-protein interactions in Saccharomyces cerevisiae. Nature 2000, 403:623-627.
11. Guo Y., Yu L., Wen Z., Li M. Using support vector machine combined with auto covariance to predict proteinprotein interactions from protein sequences. Nucleic Acids Res. 2008, 36:3025-3030.
12. You Z.-H., Yin Z., Han K., Huang D.-S., Zhou X. A semi-supervised learning approach to predict synthetic genetic interactions by combining functional and topological properties of functional gene network. BMC Bioinform. 2010, 11.
13. You Z.-H., Lei Y.-K., Gui J., Huang D.-S., Zhou X. Using manifold embedding for assessing and predicting protein interactions from high-throughput experimental data. Bioinformatics 2010, 26:2744-2751.
14. Xia J.-F., You Z.-H., Wu M., Wang S.-L., Zhao X.-M. Improved method for predicting pi-turns in proteins using a two-stage classifier. Protein Pept. Lett. 2010, 17:1117-1122.
15. Lei Y.-K., You Z.-H., Ji Z., Zhu L., Huang D.-S. Assessing and predicting protein interactions by combining manifold embedding with multiple information integration. Bmc Bioinform. 2012, 13.
16. You Z.-H., Li L., Yu H., Chen S., Wang S.-L. Increasing reliability of protein interactome by combining heterogeneous data sources with weighted network topological metrics. Advanced Intelligent Computing Theories and Applications 2010, 657-663. D.S. Huang, Z.M. Zhao, V. Bevilacqua, J.C. Figueroa (Eds.).
17. Zhao X.M., Wang R.S., Chen L., Aihara K. Uncovering signal transduction networks from high-throughput data by integer linear programming. Nucleic Acids Res. 2008, 36.
18. Zhao X.M., Li X., Chen L., Aihara K. Protein classification with imbalanced data. Proteins 2008, 70:1125-1132.
19. Zhao X.M., Zhang X.W., Tang W.H., Chen L.N. FPPI: Fusarium graminearum protein-protein interaction database. J. Proteome Res. 2009, 8:4714-4721.
20. Qi Y.J., Klein-Seetharaman J., Bar-Joseph Z. Random Forest Similarity for Protein-Protein Interaction Prediction from Multiple Sources 2005, World Scientific Publ Co Pte Ltd., Singapore.
21. Matsuzaki Yuri, Matsuzaki Yusuke, Sato Toshiyuki, Akiyama Yutaka In silico screening of protein-protein interactions with all-to-all rigid docking and clustering: an application to pathway analysis. J. Bioinform. Comput. Biol. 2009, 7:991-1012.
22. Ohue M., Matsuzaki Y., Akiyama Y. Docking-calculation-based method for predicting protein-RNA interactions. Genome Inform. 2011, 25:25-39.
23. Yang L., Xia J.F., Gui J. Prediction of protein-protein interactions from protein sequence using local descriptors. Protein Pept. Lett. 2010, 17:1085-1090.
24. Shen J., Zhang J., Luo X., Zhu W., Yu K., Chen K., Li Y., Jiang H. Predictina protein-protein interactions based only on sequences information. Proc. Natl. Acad. Sci. U.S.A 2007, 104:4337-4341.
25. Zhao X.M., Chen L.N., Aihara K. A discriminative approach for identifying domain-domain interactions from protein-protein interactions. Proteins 2010, 78:1243-1253.
26. Shi M.-G., Xia J.-F., Li X.-L., Huang D.-S. Predicting protein-protein interactions from sequence using correlation coefficient and high-quality interaction dataset. Amino Acids 2010, 38:891-899.
27. Xia J.-F., Han K., Huang D.-S. Sequence-based prediction of protein-protein interactions by means of rotation forest and autocorrelation descriptor. Protein Pept. Lett. 2010, 17:137-145.
28. Collobert R., Bengio S., Bengio Y. A parallel mixture of SVMs for very large scale problems. Neural Comput. 2002, 14:1105-1114.
29. Zanghirati G., Zanni L. A parallel solver for large quadratic programs in training support vector machines. Parallel Comput. 2003, 29:535-551.
30. Syed N.A., Liu H., Sung K.K. Incremental learning with support vector machines. Proceedings of the International Conference on Knowledge Discovery and Data Mining (KDD-99 1999.
31. Yuri M., Nobuyuki U., Masahito O., Takehiro S., Toshiyuki S., Takashi I., Yutaka.A. MEGADOCK 3.0: a high performance protein-protein interaction prediction software using hybrid parallel computing for petascale supercomputing environments. Source Code Biol. Med. 2013, 8(1).
32. Catanzaro B., Sundaram N., Keutzer K. Fast support vector machine training and classification on graphics processors. Proceedings of the 25th International Conference on Machine Learning 2008, 104-111.
33. Do T.-N., Nguyen V.-H., Poulet F. Speed up SVM algorithm for massive classification tasks. Adv. Data Min. Appl. 2008, 5139.
34. Dean J., Ghemawat S. Mapreduce: simplified data processing on large clusters. Commun. ACM 2008, 51:107-113.
35. Pan X.Y., Zhang Y.N., Shen H.B. Large-scale prediction of human protein-protein interactions from amino acid sequence based on latent topic features. J. Proteome Res. 2010, 9:4992-5001.
36. Smialowski P., Pagel P., Wong P., Brauner B., Dunger I., Fobo G., Frishman G., Montrone C., Rattei T., Frishman D., Ruepp A. The Negatome database: a reference set of non-interacting protein pairs. Nucleic Acids Res. 2010, 38:D540-D544.
37. Moran P.A.P. Notes on continuous stochastochastic phenomena. Biometrika 1950, 37:17-23.
38. Broto P., Moreau G., Vandicke C. Molecular structures:perception, autocorrelation descriptor and SAR studies. Eur. J. Med. Chem. 1984, 19:66-70.
39. Xia J.F., Han K., Huang D.S. Sequence-based prediction of protein-protein interactions by means of rotation forest and autocorrelation descriptor. Protein Pept. Lett. 2010, 17:137-145.
40. Kawashima S., Pokarowski P., Pokarowska M., Kolinski A., Katayama T., Kanehisa M. AAindex: amino acid index database, progress report. Nucleic Acids Res. 2008, 36(2008):D202-D205.
41. Cortes C., Vapnik V. Suppot vector networks. Mach. Learn. 1995, 20:273-297.