Secondary structure prediction with support vector machines
暂无分享,去创建一个
Bernard F. Buxton | Liam J. McGuffin | David T. Jones | Jonathan J. Ward | David T. Jones | B. Buxton | L. McGuffin | J. J. Ward
[1] W. Kabsch,et al. Dictionary of protein secondary structure: Pattern recognition of hydrogen‐bonded and geometrical features , 1983, Biopolymers.
[2] B. Rost,et al. Prediction of protein secondary structure at better than 70% accuracy. , 1993, Journal of molecular biology.
[3] Martin A. Riedmiller,et al. A direct adaptive method for faster backpropagation learning: the RPROP algorithm , 1993, IEEE International Conference on Neural Networks.
[4] Christopher M. Bishop,et al. Neural networks for pattern recognition , 1995 .
[5] Bernhard Schölkopf,et al. Improving the Accuracy and Speed of Support Vector Machines , 1996, NIPS.
[6] Thomas L. Madden,et al. Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. , 1997, Nucleic acids research.
[7] Robert Tibshirani,et al. Classification by Pairwise Coupling , 1997, NIPS.
[8] Vladimir Vapnik,et al. Statistical learning theory , 1998 .
[9] B. Rost,et al. Protein structure prediction , 1998 .
[10] J. C. BurgesChristopher. A Tutorial on Support Vector Machines for Pattern Recognition , 1998 .
[11] Thorsten Joachims,et al. Making large scale SVM learning practical , 1998 .
[12] Richard Hughey,et al. Hidden Markov models for detecting remote protein homologies , 1998, Bioinform..
[13] John Platt,et al. Probabilistic Outputs for Support vector Machines and Comparisons to Regularized Likelihood Methods , 1999 .
[14] B. Rost. Twilight zone of protein sequence alignments. , 1999, Protein engineering.
[15] B. Rost,et al. A modified definition of Sov, a segment‐based measure for protein secondary structure prediction assessment , 1999, Proteins.
[16] G J Barton,et al. Evaluation and improvement of multiple sequence methods for protein secondary structure prediction , 1999, Proteins.
[17] D T Jones,et al. Protein secondary structure prediction based on position-specific scoring matrices. , 1999, Journal of molecular biology.
[18] Gunnar Rätsch,et al. Engineering Support Vector Machine Kerneis That Recognize Translation Initialion Sites , 2000, German Conference on Bioinformatics.
[19] Nello Cristianini,et al. An Introduction to Support Vector Machines and Other Kernel-based Learning Methods , 2000 .
[20] C Sander,et al. Third generation prediction of secondary structures. , 2000, Methods in molecular biology.
[21] D Haussler,et al. Knowledge-based analysis of microarray gene expression data by using support vector machines. , 2000, Proceedings of the National Academy of Sciences of the United States of America.
[22] Alexander J. Smola,et al. Advances in Large Margin Classifiers , 2000 .
[23] Tom Downs,et al. Exact Simplification of Support Vector Solutions , 2002, J. Mach. Learn. Res..
[24] Volker A. Eyrich,et al. EVA: Large‐scale analysis of secondary structure prediction , 2001, Proteins.
[25] S. Hua,et al. A novel method of protein secondary structure prediction with high segment overlap measure: support vector machine approach. , 2001, Journal of molecular biology.
[26] Pierre Baldi,et al. Improving the prediction of protein secondary structure in three and eight classes using recurrent neural networks and profiles , 2002, Proteins.
[27] Chih-Jen Lin,et al. A comparison of methods for multiclass support vector machines , 2002, IEEE Trans. Neural Networks.
[28] Liam J McGuffin,et al. Benchmarking secondary structure prediction for fold recognition , 2003, Proteins.
[29] Liam J. McGuffin,et al. Benchmarking protein secondary structure prediction for protein fold recognition , 2003 .
[30] C. Sugnet,et al. Knowledge-based Analysis of Mi roarray Gene Expression Data , 2007 .