A novel method based on new adaptive LVQ neural network for predicting protein-protein interactions from protein sequences

Journal of Theoretical Biology

Volumn 336, Issue , 2013, Pages 231-239

  Yousef, Abdulaziz ^a   Moghadam Charkari, Nasrollah ^a  

^a TARBIAT MODARES UNIVERSITY   (Iran)

Author keywords

Classifier combination; Feature extraction; Learning vector quantization neural network; Principal components analysis; Protein protein interaction prediction

Indexed keywords

ACCURACY ASSESSMENT; ALGORITHM; AMINO ACID; ARTIFICIAL NEURAL NETWORK; PHYSICOCHEMICAL PROPERTY; PRINCIPAL COMPONENT ANALYSIS; PROTEIN;

ACCURACY; AMINO ACID SEQUENCE; ARTICLE; ARTIFICIAL NEURAL NETWORK; CLASSIFICATION; CLASSIFIER; COMPUTER PREDICTION; DATA PROCESSING; FALSE POSITIVE RESULT; HELICOBACTER PYLORI; LEARNING VECTOR QUANTIZATION NEURAL NETWORK; PHYSICAL CHEMISTRY; PRINCIPAL COMPONENT ANALYSIS; PRIORITY JOURNAL; PROTEIN PROTEIN INTERACTION; SACCHAROMYCES CEREVISIAE; VALIDITY; ALGORITHM; HUMAN; METABOLISM; PROCEDURES; PROTEIN ANALYSIS; PROTEIN DATABASE;

HELICOBACTER PYLORI;

ALGORITHMS; AMINO ACID SEQUENCE; DATABASES, PROTEIN; HELICOBACTER PYLORI; HUMANS; NEURAL NETWORKS (COMPUTER); PRINCIPAL COMPONENT ANALYSIS; PROTEIN INTERACTION MAPPING; SACCHAROMYCES CEREVISIAE;

EID: 84884590545     PISSN: 00225193     EISSN: 10958541     Source Type: Journal    
DOI: 10.1016/j.jtbi.2013.07.001     Document Type: Article

References (49)

1. von Mering C., Krause R., Snel B., et al. Comparative assessment of large-scale data sets of protein-protein interactions. Nature 2002, 417:399-403.
2. Caruana R., Lawrence S., Giles L. Overfitting in neural nets: backpropagation, conjugate gradient, and early stopping. Advances in Neural Information Processing Systems 2001, 402-408.
3. Charton M., Charton B.I. The structural dependence of amino acid hydrophobicity parameters. Journal of Theoretical Biology 1982, 99:629-644.
4. Chen X.-W., Liu M. Prediction of protein-protein interactions using random decision forest framework. Bioinformatics 2005, 21:4394-4400.
5. Chothia C. The nature of the accessible and buried surfaces in proteins. Journal of Molecular Biology 1976, 105:1-12.
6. DeSieno, D., 1988. Adding a conscience to competitive learning. In Proceedings of IEEE International Conference on Neural Networks. pp. 117-124.
7. Eisenberg D., McLachlan A.D. Solvation energy in protein folding and binding. Nature 1986, 319:199-203.
8. Enright A.J., et al. Protein interaction maps for complete genomes based on gene fusion events. Nature 1999, 402:86-90.
9. FAUCHÈRE J.L., et al. Amino acid side chain parameters for correlation studies in biology and pharmacology. International Journal of Peptide and Protein Research 1988, 32:269-278.
10. Feng Z.P., Zhang C.T. Prediction of membrane protein types based on the hydrophobic index of amino acids. Journal of Protein Chemistry 2000, 19:269-275.
11. Fields S, Song O. A novel genetic system to detect protein protein interactions. Nature 1989, 340:245-246.
12. Gavin A.C., Aloy P., Grandi P., Krause R., Boesche M., Marzioch M., Rau C, Jensen L.J., Bastuck S., Dumpelfeld B., et al. Proteome survey reveals modularity of the yeast cell machinery. Nature 2006, 440:631-636.
13. Ge H., Walhout A., Vidal M. Integrating 'omic' information: a bridge between genomics and systems biology. Trends in Genetics 2003, 19:551-560.
14. Grantham R. Amino acid difference formula to help explain protein evolution. Science 1974, 185:862-864.
15. Guo Y., et al. Using support vector machine combined with auto covariance to predict protein-protein interactions from protein sequences. Nucleic Acids Research 2008, 36:3025-3030.
16. Hopp T.P., Woods K.R. Prediction of protein antigenic determinants from amino acid sequences. Proceedings of the National Academy of Sciences of the United States of America 1981, 78:3824-3828.
17. Ito T., Chiba T., Ozawa R., Yoshida M., Hattori, Sakaki Y. A comprehensive two-hybrid analysis to explore the yeast protein interactome. Proceedings of the National Academy of Sciences of the United States of America 2001, 98:4569-4574.
18. Janin J. Surface and inside volumes in globular proteins. Nature 1979, 277:491-492.
19. Ji J., et al. Improved ant colony optimization for detecting functional modules in protein-protein interaction networks. Information Computing and Applications 2012, 404-413. Springer, Berlin Heidelberg. C. Liu, L. Wang, A. Yang (Eds.).
20. JO H. Heat capacities, absolute entropies, and entropies of formation of amino acids and related compounds. Handbook of biochemistry 1970, B60-B61. Chemical Rubber Co., Cleveland. Second ed. H.A. Sober (Ed.).
21. Jolliffe I. Principal Component Analysis. Encyclopedia of Statistics in Behavioral Science 2005, University of Aberdeen, Aberdeen, UK, 15 OCT 2005 DOI: 10.1002/0470013192.bsa501.
22. Garel J.P. Coefficients de partage d'aminoacides, nucleobases, nucleosides et nucleotides dans un systeme solvant salin. Journal of Chromatography 1973, 78:381-391.
23. Kohonen T. Self-Organization and Associative Memory 1988, 8:312. Springer-Verlag Berlin Heidelberg New York Also Springer Series in Information Sciences.
24. Kohonen, T., Barna, G. ,Chrisley, R., 1988. Statistical pattern recognition with neural networks: Benchmarking studies. IEEE, 61-68.
25. Le D.-H., Kwon Y.-K. Neighbor-favoring weight reinforcement to improve random walk-based disease gene prioritization. Computational Biology and Chemistry 2013, 44:1-8.
26. Martin S., Roe D., Faulon J.-L. Predicting protein-protein interactions using signature products. Bioinformatics 2005, 21:218-226.
27. Deng Minghua, Zhang Kui, Mehta Shipra, Chen Ting, Sun Fengzhu Prediction of protein function using protein-protein interaction data. Computational Biology 2003, 10:947-960.
28. Mishra G.R., et al. Human protein reference database. Nucleic Acids Research 2006, 34:D411-D414.
29. Navlakha S., Gitter A., Bar-Joseph Z. A network-based approach for predicting missing pathway interactions. PLOS Computational Biology 2012, 8:e1002640.
30. Nguyen G., et al. Learning pattern classification tasks with imbalanced data sets. Pattern Recognition 2009, 193-208. P. Yin (Ed.).
31. Pearson K. LIII.On lines and planes of closest fit to systems of points in space. Philosophical Magazine Series 1901, 6(2):559-572.
32. Peri S., et al. Development of human protein reference database as an initial platform for approaching systems biology in humans. Genome Research 2003, 13:2363-2371.
33. Prabhakaran M., Ponnuswamy P.K. Shape and surface features of globular proteins. Macromolecules 1982, 15:314-320.
34. Prechelt L. Proben1: a set of neural network benchmark problems and benchmarking rules. Technical Report 1994, 21:94. Fakultät für Informatik, University of Karlsruhe, Karlsruhe, Germany.
35. Qi Y., Klein-Seetharaman J., Bar-Joseph Z. A mixture of feature experts approach for protein-protein interaction prediction. BMC Bioinformatics 2007, 8(Suppl 10):S6.
36. Qi Y., Bar-Joseph Z., Klein-Seetharaman J. Evaluation of different biological data and computational classification methods for use in protein interaction prediction. Proteins: Structure, Function, and Bioinformatics 2006, 63:490-500.
37. Rosin P.L., Fierens F. Improving neural network generalisation. IEEE 1995, 1255-1257.
38. Shen J., et al. Predicting protein-protein interactions based only on sequences information. Proceedings of the National Academy of Sciences of the United States of America 2007, 104:4337-4341.
39. Smialowski P., et al. The Negatome database: a reference set of non-interacting protein pairs. Nucleic Acids Research 2010, 38:D540-D544.
40. Sokal R.R., Thomson B.A. Population structure inferred by local spatial autocorrelation: an example from an Amerindian tribal population. American Journal of Physical Anthropology 2006, 129:121-131.
41. Sweet R.M., Eisenberg D. Correlation of sequence hydrophobicities measures similarity in three-dimensional protein structure. Journal of Molecular Biology 1983, 171:479-488.
42. Vakil-Baghmisheh M.-T., Pavešić N. Premature clustering phenomenon and new training algorithms for LVQ. Pattern Recognition 2003, 36:1901-1912.
43. Von Storch H., Zwiers F.W. Statistical Analysis in Climate Research 2002, Cambridge University Press, Cambridge, UK.
44. Xia J.F., Han K., Huang D.S. Sequence-based prediction of protein-protein interactions by means of rotation forest and autocorrelation descriptor. Protein and Peptide Letters 2010, 17:137-145.
45. Xia J.F., Zhao X.M., Huang D.S. Predicting protein-protein interactions from protein sequences using meta predictor. Amino Acids 2010, 39:1595-1599.
46. Ho Y., Gruhler A., Heilbut A., Bader G.D., Moore L., Adams S.L., Millar A., et al. Systematic identification of protein complexes in Saccharomyces cerevisiae by mass spectrometry. Nature 2002, 415:180-183.
47. Yang L., Xia J.F., Gui J., Huang D.S. Prediction of protein-protein interactions from protein sequence using local descriptors. Protein and Peptide Letters 2010, 17:1085-1090.
48. Yu C.Y., et al. Predicting protein-protein interactions in unbalanced data using the primary structure of proteins. BMC Bioinformatics 2010, 11:167.
49. Yu J., et al. Simple sequence-based kernels do not predict protein-protein interactions. Bioinformatics 2010, 26:2610-2614.