Bayesian network multi-classifiers for protein secondary structure prediction

Artificial Intelligence in Medicine

Volumn 31, Issue 2, 2004, Pages 117-136

  Robles, Víctor ^a   Larrañaga, Pedro ^b   Peña, José M ^a   Menasalvas, Ernestina ^a   Pérez, María S ^a   Herves, Vanessa ^a   Wasilewska, Anita ^c  

^a UNIVERSIDAD POLITÉCNICA DE MADRID   (Spain)

^b UNIVERSITY OF THE BASQUE COUNTRY UPV EHU   (Spain)

^c Stony Brook University   (United States)

Author keywords

Bayesian networks; Machine learning; Multi classifier; Pazzani EDA; Protein secondary structure prediction; Stacked generalization; Supervised classification

Indexed keywords

FUNCTIONS; INTERNET; MATHEMATICAL MODELS; MOLECULAR STRUCTURE; PROTEINS;

COMPONENT CLASSIFIERS; SECONDARY STRUCTURE;

MEDICINE;

ACCURACY; ARTICLE; BAYES THEOREM; INTERNET; PREDICTION; PRIORITY JOURNAL; PROTEIN ANALYSIS; PROTEIN SECONDARY STRUCTURE; VALIDATION PROCESS;

BAYES THEOREM; DATABASES, FACTUAL; FORECASTING; INTERNET; PROTEIN STRUCTURE, SECONDARY; PROTEIN STRUCTURE, TERTIARY; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY;

EID: 3042618109     PISSN: 09333657     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.artmed.2004.01.009     Document Type: Article

References (35)

1. Schmidler S., Liu J., Brutlag D. Bayesian segmentation of protein secondary structure. J Comput Biol. 2(1-2):2000;233-248
2. Altschul S.F., Madden T.L., Schaffer A.A., Zhang J., Zhang Z., Miller W., et al. Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucl Acids Res. 25(17):1997;3389-3402
3. Jones D. Protein secondary structure prediction based on position-specific scoring matrices. J Mol Biol. 292:1999;195-202
4. Pollastri G, Przybylski D, Rost B, Baldi P. Improving the prediction of protein secondary strucure in three and eight classes using recurrent neural networks and profiles, Proteins (2001) 228-235.
5. Wolpert D.H. Stacked generalization. Neural Networks. 5:1992;241-259
6. Ho T., Srihati S. Decision combination in multiple classifier systems. IEEE Trans Pattern Anal Machine Learning. 11:1994;63-90
7. Hobohm U., Scharf M., Schneider R., Sander C. Selection of a representative set of structures from the brookhaven protein data bank. Protein Sci. 1:1992;409-417
8. Rost B., Sander C. Prediction of protein secondary structure at better than 70% accuracy. J Mol Biol. 232:1993;584-599
9. Cuff J., Barton G. Evaluation and improvement of multiple sequence methods for protein secondary structure prediction. Proteins. 34:1999;508-519
10. Rost B., Eyrich V. EVA: large-scale analysis of secondary structure prediction. Proteins. 5:2001;192-199
11. Berman H., Westbrook J., Feng Z., Gilliand G., Bhat T., Weissig H., et al. The protein data bank. Nucl Acid Res. 28:2000;235-242
12. Kabsch W., Sander C. Dictionary of protein secondary structure: pattern recognition of hydrogen bonded and geometrical features. Biopolymers. 22:1983;2577-2637
13. Matthews F. The structure, function and evolution of cytochromes. Prog. Biophys. Mol. Biol. 45:1985;1-56
14. Rost B., Sander C., Schneider R. Redefining the goals of protein secondary structure prediction. J Mol Biol. 235:1994;13-26
15. Zemla A., Venclovas C., Fidelis K., Rost B. A modified definition of SOV, a segment-based measure for protein secondary structure prediction assessment. Proteins. 34:1999;220-223
16. Cuff J., Clamp M., Siddiqui A., Finlay M., Barton G. JPRED: A consensus secondary structure prediction server. Bioinformatics. 14:1998;892-893
17. King R., Sternberg M. Identification and application of the concepts important for accurate and reliable protein secondary structure prediction. Protein Sci. 5:1996;2298-2310
18. Salamov A., Solovyev V. Prediction of protein secondary structure by combining nearest-neighbor algorithms and multiple sequence alignment. J Mol Biol. 247:1995;11-15
19. Frishman D., Argos P. 75% accuracy in protein secondary structure prediction. Proteins. 27:1997;329-335
20. Zvelebil M., Barton G., Taylor W., Sternberg M. Prediction of protein secondary structure and active sites using the alignment of homologous sequences. J Mol Biol. 195:1987;957-961
21. Barton G., Taylor W. Prediction of protein secondary structure and active sites using the alignment of homologous sequences. J Mol Biol. 195:1988;957-961
22. Baldi P, Brunak S, Frasconi P, Pollastri G, Soda G. Bidirectional dynamics for protein secondary structure prediction. In: Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence (IJCAI99). Stockholm, Sweden, 1999.
23. Baldi P., Brunak S., Frasconi P., Pollastri G., Soda G. Exploiting the past and the future in protein secondary structure prediction. Bioinformatics. 15:1999;937-946
24. Rost B., Sander C., Schneider R. PHD - an automatic mail server for protein secondary structure prediction. Comput Appl Biosci. 10:1994;53-60
25. Ouali M., King R. Cascaded multiple classifiers for secondary structure prediction. Protein Sci. 9:2000;1162-1176
26. Garnier J., Osguthorpe D., Robson B. Analysis of the accuracy and implications of simple methods for predicting the secondary structure of globular proteins. J Mol Biol. 120:1978;97-120
27. Gibrat J., Garnier J., Robson B. Further developments of protein secondary structure prediction using information theory. New parameters and consideration of residue pairs. J Mol Biol. 198:1987;425-443
28. Qian N., Sejnowski T. Predicting the secondary structure of globular proteins using neural network models. J Mol Biol. 202:1988;865-884
29. Karplus K, Karchin R, Draper J, Casper J, Mandel-Gutfreund Y, Diekhans M, et al. Combining local-structure, fold-recognition, and new-fold methods for protein structure prediction, ProteinsMayo.
30. Sierra B., Serrano N., Larrañaga P., Plasencia E., Inza I., Jimenez J., et al. Using Bayesian networks in the construction of a bi-level multi-classifier. A case study using intensive care unit patient data. Artif Intell Med. 22:2001;233-248
31. Duda R, Hart P. Pattern classification and scene analysis. Wiley; 1973.
32. Hand D., Yu K. Idiot's Bayes - not so stupid after all? Inter Stat Rev. 69(3):2001;385-398
33. Robles V, Larrañaga P, Peña J, Menasalvas E, Pérez M. Interval estimation naïve Bayes. In: Lecture notes in computer science, advances in intelligent data analysis. Berlin, Germany; 2003, in press.
34. Larrañaga P, Lozano J. Estimation of distribution algorithms. A new tool for evolutionary computation. Kluwer Academic Publisher; 2002.
35. Pazzani M. Searching for dependencies in Bayesian classifiers. In: Learning from data: artificial intelligence and statistics V; 1997, pp. 239-48.