Predicting protein-RNA interaction amino acids using random forest based on submodularity subset selection

Computational Biology and Chemistry

Volumn 53, Issue PB, 2014, Pages 324-330

  Pan, Xiaoyong ^a,b   Zhu, Lin ^c   Fan, Yong Xian ^d   Yan, Junchi ^e  

^a UNIVERSITY OF COPENHAGEN   (Denmark)

^b University of Copenhagen   (Denmark)

^c SHANGHAI UNIVERSITY OF ELECTRIC POWER   (China)

^d GUILIN UNIVERSITY OF ELECTRONIC TECHNOLOGY   (China)

^e SHANGHAI JIAO TONG UNIVERSITY   (China)

Author keywords

Evolution information; Protein RNA interaction site; Random forest; Sample imbalance; Submodularity subset selection

Indexed keywords

AMINO ACIDS; ARTIFICIAL INTELLIGENCE; BINDING SITES; BIOLOGY; DECISION TREES; FORECASTING; LEARNING SYSTEMS; PROTEINS; RNA; SET THEORY; SITE SELECTION; TRANSCRIPTION;

BIOLOGICAL PROCESS; EVOLUTION INFORMATION; IMPORTANCE ANALYSIS; MACHINE LEARNING MODELS; PROTEIN-RNA INTERACTION; RANDOM FORESTS; STATE-OF-THE-ART METHODS; SUBSET SELECTION;

BIOSYNTHESIS;

EID: 84913530064     PISSN: 14769271     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.compbiolchem.2014.11.002     Document Type: Article

References (47)

1. J. Allers, and Y. Shamoo Structure-based analysis of protein-RNA interactions using the program ENTANGLE J. Mol. Biol. 311 2001 75 86
2. S.F. Altschul, T.L. Madden, A.A. Schäffer, J. Zhang, Z. Zhang, W. Miller, and D.J. Lipman Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucl. Acids Res. 25 1997 3389 3402
3. A. Bairoch, and R. Apweiler The SWISS-PROT protein sequence database and its supplement TrEMBL in 2000 Nucl. Acids Res. 28 2000 45 48
4. D.P. Bartel MicroRNAs: target recognition and regulatory functions Cell 136 2009 215 233
5. L. Breiman Random forests Mach. Learning 45 2001 5 32
6. Y.C. Chen, and C. Lim Predicting RNA-binding sites from the protein structure based on electrostatics, evolution and geometry Nucl. Acids Res. 36 2008 e29
7. C.W. Cheng, E. Su, J.K. Hwang, T.Y. Sung, and W.L. Hsu Predicting RNA-binding sites of proteins using support vector machines and evolutionary information BMC Bioinform. 9 2008 S6
8. T. Fukunaga, H. Ozaki, G. Terai, K. Asai, W. Iwasaki, and H. Kiryu CapR: revealing structural specificities of RNA-binding protein target recognition using CLIP-seq data Genome Biol. 15 2014 R16
9. E. Gabrilovich, and S. Markovitch Text categorization with many redundant features: using aggressive feature selection to make SVMs competitive with C4.5 Advances 69 2004 41
10. E. Gasteiger, C. Hoogland, A. Gattiker, S. Duvaud, M.R. Wilkins, R.D. Appel, and A. Bairoch Protein Identification and Analysis Tools on the ExPASy Server The Proteomics Protocols Handbook 2005 571 607
11. Y. Guo, I. Silins, U. Stenius, and A. Korhonen Active learning-based information structure analysis of full scientific articles and two applications for biomedical literature review Bioinformatics (Oxford, England) 2013 1 7
12. L.M. Hellman, and M.G. Fried Electrophoretic mobility shift assay (EMSA) for detecting protein-nucleic acid interactions Nat. Protocols 2 2007 1849 1861
13. S. Hormoz Amino acid composition of proteins reduces deleterious impact of mutations Sci. Rep. 3 2013 2919
14. D.T. Jones Protein secondary structure prediction based on position-specific scoring matrices J. Mol. Biol. 292 1999 195 202
15. W. Kabsch, and C. Sander Dictionary of protein secondary structure: pattern recognition of hydrogen-bonded and geometrical features Biopolymers 22 1983 2577 2637
16. O.T.P. Kim, K. Yura, and N. Go Amino acid residue doublet propensity in the protein-RNA interface and its application to RNA interface prediction Nucl. Acids Res. 34 2006 6450 6460
17. S. Kishore, L. Jaskiewicz, L. Burger, J. Hausser, M. Khorshid, and M. Zavolan A quantitative analysis of CLIP methods for identifying binding sites of RNA-binding proteins Nat. Methods 8 2011 559 564
18. A. Krause, B. McMahan, C. Guestrin, and A. Gupta Robust submodular observation selection J. Mach. Learning Res. 9 2008 2761 2801
19. B.A. Lewis, R.R. Walia, M. Terribilini, J. Ferguson, C. Zheng, V. Honavar, and D. Dobbs PRIDB: a protein-RNA interface database Nucl. Acids Res. 39 2011 D277 D282
20. H. Lin, and J. Bilmes How to select a good training-data subset for transcription: submodular active selection for sequences Interspeech 2009 2009 18 21
21. Z.-P. Liu, L.-Y. Wu, Y. Wang, X.-S. Zhang, and L. Chen Prediction of protein-RNA binding sites by a random forest method with combined features Bioinformatics (Oxford, England) 26 2010 1616 1622
22. B.M. Lunde, C. Moore, and G. Varani RNA-binding proteins: modular design for efficient function Nat. Rev. Mol. Cell Biol. 8 2007 479 490
23. J.A. Marsh, and S.A. Teichmann Relative solvent accessible surface area predicts protein conformational changes upon binding Structure 19 2011 859 867
24. R.C. Moore, and W. Lewis Intelligent selection of language model training data Proceedings of the ACL 2010 Conference Short Papers 2010 220 224
25. L. Pérez-Cano, and J. Fernández-Recio Optimal protein-RNA area, OPRA: a propensity-based method to identify RNA-binding sites on proteins Proteins 78 2010 25 35
26. X.-Y. Pan, and H.-B. Shen Robust prediction of B-factor profile from sequence using two-stage SVR based on random forest feature selection Protein Peptide Lett. 16 2009 1447 1454
27. X.-Y. Pan, Y.-N. Zhang, and H.-B. Shen Large-scale prediction of human protein-protein interactions from amino acid sequence based on latent topic features J. Proteome Res. 9 2010 4992 5001
28. F. Pedregosa, R. Weiss, and M. Brucher Scikit-learn: machine learning in Python J. Mach. Learning Res. 12 2011 2825 2830
29. H.C. Peng, F.H. Long, and C. Ding Feature selection based on mutual information: criteria of max-dependency, max-relevance, and min-redundancy IEEE Trans. Pattern Anal. Mach. Intell. 27 2005 1226 1238
30. D. Ray, H. Kazan, E.T. Chan, L. Peña Castillo, S. Chaudhry, S. Talukder, B.J. Blencowe, Q. Morris, and T.R. Hughes Rapid and systematic analysis of the RNA recognition specificities of RNA-binding proteins Nat. Biotechnol. 27 2009 667 670
31. B.M. Rhode, K. Hartmuth, H. Urlaub, and R. Luhrmann Analysis of site-specific protein-RNA cross-links in isolated RNP complexes, combining affinity selection and mass spectrometry RNA (New York, N.Y.) 9 2003 1542 1551
32. J. Shotton, A. Fitzgibbon, M. Cook, T. Sharp, M. Finocchio, R. Moore, A. Kipman, and A. Blake Real-time human pose recognition in parts from single depth images CVPR 2011 2011 1297 1304
33. A. Shulman-Peleg, M. Shatsky, R. Nussinov, and H.J. Wolfson Prediction of Interacting Single-Stranded RNA Bases by Protein-Binding Patterns J. Mol. Biol. 379 2008 299 316
34. R.L. Skalsky, D.L. Corcoran, J.D. Keene, N. Mukherjee, E. Gottwein, U. Ohler, and S. Georgiev PARalyzer: definition of RNA binding sites from PAR-CLIP short-read sequence data Genome Biol. 12 2011 R79
35. J. Song, H. Tan, A.J. Perry, T. Akutsu, G.I. Webb, J.C. Whisstock, and R.N. Pike PROSPER: an integrated feature-based tool for predicting protease substrate cleavage sites PLoS ONE 7 11 2012 e50300
36. M. Terribilini, J.D. Sander, J.-H. Lee, P. Zaback, R.L. Jernigan, V. Honavar, and D. Dobbs RNABindR: a server for analyzing and predicting RNA-binding sites in proteins Nucl. Acids Res. 35 2007 W578 W584
37. T. Tuschl Functional genomics: RNA sets the standard Nature 421 2003 220 221
38. J. Ule, K. Jensen, A. Mele, and R.B. Darnell CLIP: a method for identifying protein-RNA interaction sites in living cells Methods 37 2005 376 386
39. V.N. Vapnik The nature of statistical learning theory 8 1995
40. L. Wang, C. Huang, M.Q. Yang, and J.Y. Yang BindN+ for accurate prediction of DNA and RNA-binding residues from protein sequence features BMC Syst. Biol. 4 Suppl 1 2010 S3
41. Y. Wang, X. Chen, Z.-P. Liu, Q. Huang, Y. Wang, D. Xu, X.-S. Zhang, R. Chen, and L. Chen De novo prediction of RNA-protein interactions from sequence information Mol. Bio. Systems 9 1 2013 133 142
42. T. Wang, B. Chen, MinSoo. Kim, Y. Xie, and G. Xiao A Model-Based Approach to Identify Binding Sites in CLIP-Seq Data PLoS ONE 9 4 2014 e93248
43. Q. Wei, and R.L. Dunbrack The role of balanced training and testing data sets for binary classifiers in bioinformatics PLoS ONE 8 7 2013 e67863
44. J. Yan Graduated consistency-regularized optimization for multi-graph matching Proceedings of the IEEE International Conference on Computer Vision, Vol. 3 2014 407 422
45. J. Yan, Y. Tian, H. Zha, X. Yang, and Y. Zhang Joint optimization for consistent multiple graph matching Proceedings of the IEEE International Conference on Computer Vision 2013 1649 1656
46. M. Zaslavskiy, F. Bach, and J.-P. Vert Global alignment of protein-protein interaction networks by graph matching methods Bioinformatics (Oxford, England) 25 2009 i259 i267
47. Y.-N. Zhang, D.-J. Yu, S.-S. Li, Y.-X. Fan, Y. Huang, and H.-B. Shen Predicting protein-ATP binding sites from primary sequence through fusing bi-profile sampling of multi-view features BMC Bioinform. 13 2012 118