SVM‐Cabins: Prediction of solvent accessibility using accumulation cutoff set and support vector machine
暂无分享,去创建一个
[1] Gérard Dreyfus,et al. Single-layer learning revisited: a stepwise procedure for building and training a neural network , 1989, NATO Neurocomputing.
[2] Hahn-Ming Lee,et al. Prediction and evolutionary information analysis of protein solvent accessibility using multiple linear regression , 2005, Proteins.
[3] Shandar Ahmad,et al. NETASA: neural network based prediction of solvent accessibility , 2002, Bioinform..
[4] G J Barton,et al. Application of multiple sequence alignment profiles to improve protein secondary structure prediction , 2000, Proteins.
[5] B. Rost,et al. Prediction of protein secondary structure at better than 70% accuracy. , 1993, Journal of molecular biology.
[6] Vladimir Vapnik,et al. Statistical learning theory , 1998 .
[7] Shandar Ahmad,et al. Analysis and prediction of DNA-binding proteins and their binding residues based on composition, sequence and structural information , 2004, Bioinform..
[8] Corinna Cortes,et al. Support-Vector Networks , 1995, Machine Learning.
[9] Bernhard E. Boser,et al. A training algorithm for optimal margin classifiers , 1992, COLT '92.
[10] Jagath C Rajapakse,et al. Two‐stage support vector regression approach for predicting accessible surface areas of amino acids , 2006, Proteins.
[11] R. Russell,et al. Amino‐Acid Properties and Consequences of Substitutions , 2003 .
[12] Haesun Park,et al. Prediction of protein relative solvent accessibility with support vector machines and long‐range interaction 3D local descriptor , 2004, Proteins.
[13] M. Gromiha,et al. Real value prediction of solvent accessibility from amino acid sequence , 2003, Proteins.
[14] Miguel Figueroa,et al. Competitive learning with floating-gate circuits , 2002, IEEE Trans. Neural Networks.
[15] Chih-Jen Lin,et al. A comparison of methods for multiclass support vector machines , 2002, IEEE Trans. Neural Networks.
[16] Jung-Ying Wang,et al. Look‐up tables for protein solvent accessibility prediction and nearest neighbor effect analysis , 2004, Biopolymers.
[17] Chih-Jen Lin,et al. Formulations of Support Vector Machines: A Note from an Optimization Point of View , 2001, Neural Computation.
[18] Kevin Burrage,et al. Prediction of protein solvent accessibility using support vector machines , 2002, Proteins.
[19] Zheng Yuan,et al. Prediction of protein accessible surface areas by support vector regression , 2004, Proteins.
[20] Harpreet Kaur,et al. Real value prediction of solvent accessibility in proteins using multiple sequence alignment and secondary structure , 2005, Proteins.
[21] B. Schölkopf,et al. Advances in kernel methods: support vector learning , 1999 .
[22] Jagath C Rajapakse,et al. Prediction of protein relative solvent accessibility with a two‐stage SVM approach , 2005, Proteins.
[23] Aleksey Porollo,et al. PROTEINS: Structure, Function, and Bioinformatics 56:753–767 (2004) Accurate Prediction of Solvent Accessibility Using Neural Networks–Based Regression , 2022 .