YASSPP: Better kernels and coding schemes lead to improvements in protein secondary structure prediction

Proteins: Structure, Function and Genetics

Volumn 64, Issue 3, 2006, Pages 575-586

  Karypis, George ^a  

^a University of Minnesota   (United States)

Author keywords

Machine learning; Proteins; Structural bioinformatics1; Support vector machines

Indexed keywords

ALGORITHM; ARTICLE; PREDICTION; PRIORITY JOURNAL; PROTEIN FOLDING; PROTEIN FUNCTION; PROTEIN SECONDARY STRUCTURE; PROTEIN TERTIARY STRUCTURE; SEQUENCE HOMOLOGY; STRUCTURAL BIOINFORMATICS;

ALGORITHMS; COMPUTATIONAL BIOLOGY; DATABASES, PROTEIN; MODELS, MOLECULAR; PROTEIN STRUCTURE, SECONDARY; REPRODUCIBILITY OF RESULTS; SEQUENCE ALIGNMENT; SOFTWARE;

EID: 33746267388     PISSN: 08873585     EISSN: 10970134     Source Type: Journal    
DOI: 10.1002/prot.21036     Document Type: Article

References (33)

1. McGuffin LJ, Jones DT. Benchmarking secondary structure prediction for fold recognition. Proteins Struct Funct Genet 2003;152:166-175.
2. Rost B. Review: protein secondary structure prediction continues to rise. J Struct Biol 2001;134:204-218.
3. Salaniov AA, Solovyev W. Protein secondary structure prediction using local alignments. J Mol Biol 1997;268:31-36.
4. Frishman D, Argos P. Seventy-five percent accuracy in protein secondary structure prediction. Proteins Struct Funct Genet 1997; 27:329-335.
5. Joo K, Lee J, Kim S, Kum I, Lee, J, Lee S. Profile-based nearest neighbor method for pattern recognition. J. Korean Phys Soc 2004;54:599-604.
6. Rost B, Sander C. Prediction of protein secondary structure at better than 70% accuracy. J Mol Biol 1993;232:584-599.
7. Jones DT. Protein secondary structure prediction based on position-specific scoring matricies. J Mol Biol 1999;292:195-202.
8. Przybylski D, Rost B. Alignments grow, secondary structure prediction improves. Proteins Struct Funct Genet 2002;46:197-205.
9. Pollastri G, Przybylski D, Rost B, Baldi P. Improving the prediction of protein secondary structure in three and eight classes using recurrent neural networks and profiles. Proteins Struct Funct Genet 2002;47:228-235.
10. Cuff JA, Barton GJ. Evaluation and improvement of multiple sequence methods for protein secondary structure prediction. Proteins Struct Funct Genet 1999;34:508-519.
11. Pollastri G, McLysaght A. Porter: a new, accurate server for protein secondary structure prediction. Bioinformatics 2005;21: 1719-1720.
12. Rost B, Eyrich VA. EVA: large-scale analysis of secondary structure prediction. Proteins Struct Funct Genet Suppl 2001;5:192-199.
13. Vapnik V. Statistical learning theory. New York: John Wiley; 1998.
14. Hua S, Sun Z. A novel method of protein secondary structure prediction with high segment overlap measure: support vector machine approach. J Mol Biol 2001;308:397-407.
15. Kim H, Park H. Protein secondary structure prediction based on an improved support vector machine approach. Protein Eng 2003;16:553-560.
16. Ward JJ, McGuffin LJ, Buxton BF, Jones DT. Secondary structure prediction with support vector machines. Bioinformatics 2003;19: 1650-1655.
17. Guo J, Chen H, Sun Z, Lin Y. 2004. A novel method for protein secondary structure prediction using dual-layer svm and profiles. Proteins Struct Funct Genet 2004;54:738-743.
18. Hu H, Pan Y, Harrison R, Tai P. Improved protein secondary structure prediction using support vector machine and a new encoding scheme and an advanced tertiary classiffier. IEEE Trans NanoBiosci 2004;3:265-271.
19. Altschul SF, Madden LT, Schffer AA, Zhang J, Zhang Z, Miller W, Lipman DJ. Gapped blast and psi-blast: a new generation of protein database search programs. Nucleic Acids Res 1997;25: 3389-3402.
20. Henikoff S, Henikoff JG. Amino acid subsitution matrices from protein blocks. Proc Natl Acad Sci USA 1992;89:10915-10919.
21. Kabsch W, Sander C. Dictionary of protein secondary structure: pattern recognition of hydrogen-bonded and geometrical features. Biopolymers 1983;22:2577-2637.
22. Crooks GE, Wolfe J, Brenner SE. Measurements of protein sequence-structure correlations. Proteins Struct Funct Genet 2004;57:804-810.
23. Smola AJ, Bartlett P, Scholkopf B, Shuurmans D, editors. Probabilistic outputs for support vector machines and comparison of regularized likelihood methods. Advances in large margin classifiers. Cambridge, MA: MIT Press; 2000. p 61-74.
24. Rost B, Sander C, Schneider R. Redefining the goals of protein secondary structure prediction. J Mol Biol 1994;235:13-26.
25. Semla A, Venclovas C, Fidelis K, Rost B. A modified definition of sov, a segment-based measure for protein secondary structure prediction assessment. Proteins Struct Funct Genet 1999;34:220-223.
26. Matthews FS. The structure, function and evolution of cytochromes. Prog Biophys Mol Biol 1975;45:1-56.
27. Murzin AG, Brenner SE, Hubbard T, Chothia C. Scop: a structural classification of proteins database for the investigation of sequences and structures. J Mol Biol 1995;247:536-540.
28. Chandonia JM, Hon G, Walker NS, Conte LL, Koehl P, Levitt M, Brenner SE. The astral compendium in 2004. Nucleic Acids Res 2004;32:D189-D192.
29. Robles V, Larranaga P, Pena JM, Menasalvas E, Perez MS, Herves V, Wasilewska A. Bayesian network multi-classifiers for protein secondary structure prediction. Artif Intell Med 2004;31: 117-136.
30. Schlkopf B, Burges C, Smola A, editors. Making large-scale SVM learning practical. Advances in kernel methods-support vector learning. Cambridge, MA: MIT Press; 1999.
31. Karplus K, Barrett C, Hughey R. Hidden markov models for detecting remote protein homologies. Bioinformatics 1998;14:846-856.
32. Porollo A, Adamczak R, Wagner M, Meller J. Maximum feasibility approach for consensus classifiers: applications to protein structure prediction. CIRAS 2003.
33. Lin K, Simossis VA, Taylor WR, Heringa J. A simple and fast secondary structure prediction method using hidden neural networks. Bioinformatics 2005;21:152-159.