Multi-class protein fold recognition using support vector machines and neural networks
暂无分享,去创建一个
[1] U. Hobohm,et al. Selection of representative protein data sets , 1992, Protein science : a publication of the Protein Society.
[2] B. Rost,et al. Prediction of protein secondary structure at better than 70% accuracy. , 1993, Journal of molecular biology.
[3] U. Hobohm,et al. Enlarged representative set of protein structures , 1994, Protein science : a publication of the Protein Society.
[4] Edward C. Uberbacher,et al. Predicting Protein Folding Classes without Overly Relying on Homology , 1995, ISMB.
[5] I. Muchnik,et al. Prediction of protein folding class using global description of amino acid sequence. , 1995, Proceedings of the National Academy of Sciences of the United States of America.
[6] K. Chou,et al. Prediction of protein structural classes. , 1995, Critical reviews in biochemistry and molecular biology.
[7] Federico Girosi,et al. An improved training algorithm for support vector machines , 1997, Neural Networks for Signal Processing VII. Proceedings of the 1997 IEEE Signal Processing Society Workshop.
[8] J. C. BurgesChristopher. A Tutorial on Support Vector Machines for Pattern Recognition , 1998 .
[9] C. Chothia,et al. Assessing sequence comparison methods with reliable structurally identified distant evolutionary relationships. , 1998, Proceedings of the National Academy of Sciences of the United States of America.
[10] Thorsten Joachims,et al. Making large scale SVM learning practical , 1998 .
[11] Jason Weston,et al. Multi-Class Support Vector Machines , 1998 .
[12] D. Haussler,et al. Sequence comparisons using multiple sequences detect three times as many remote homologues as pairwise methods. , 1998, Journal of molecular biology.
[13] R. Durbin,et al. Biological sequence analysis: Background on probability , 1998 .
[14] David Haussler,et al. Using the Fisher Kernel Method to Detect Remote Protein Homologies , 1999, ISMB.
[15] I. Muchnik,et al. Recognition of a protein fold in the context of the Structural Classification of Proteins (SCOP) classification. , 1999, Proteins.
[16] B. Schölkopf,et al. Advances in kernel methods: support vector learning , 1999 .
[17] Chris Sander,et al. Protein folds and families: sequence and structure alignments , 1999, Nucleic Acids Res..
[18] I. Muchnik,et al. Recognition of a protein fold in the context of the SCOP classification , 1999 .
[19] David C. Jones,et al. GenTHREADER: an efficient and reliable protein fold recognition method for genomic sequences. , 1999, Journal of molecular biology.
[20] Tim J. P. Hubbard,et al. SCOP: a Structural Classification of Proteins database , 1999, Nucleic Acids Res..
[21] Pierre Baldi,et al. Assessing the accuracy of prediction algorithms for classification: an overview , 2000, Bioinform..
[22] Vladimir N. Vapnik,et al. The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.
[23] James E. Bray,et al. Assigning genomic sequences to CATH , 2000, Nucleic Acids Res..
[24] 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.
[25] Pierre Baldi,et al. Bioinformatics - the machine learning approach (2. ed.) , 2000 .