Bayesian Protein Secondary Structure Prediction With Near-Optimal Segmentations
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[1] F. Jelinek. Fast sequential decoding algorithm using a stack , 1969 .
[2] Simon Cawley,et al. HMM sampling and applications to gene finding and alternative splicing , 2003, ECCB.
[3] M J Sternberg,et al. A simple method to generate non-trivial alternate alignments of protein sequences. , 1991, Journal of molecular biology.
[4] Silvio C. E. Tosatto,et al. MANIFOLD: protein fold recognition based on secondary structure, sequence similarity and enzyme classification. , 2003, Protein engineering.
[5] B. Rost. Twilight zone of protein sequence alignments. , 1999, Protein engineering.
[6] M. Waterman,et al. A new algorithm for best subsequence alignments with application to tRNA-rRNA comparisons. , 1987, Journal of molecular biology.
[7] Yücel Altunbasak,et al. Protein secondary structure prediction with semi Markov HMMs , 2004, The 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.
[8] V. Thorsson,et al. HMMSTR: a hidden Markov model for local sequence-structure correlations in proteins. , 2000, Journal of molecular biology.
[9] Pierre Baldi,et al. Three-stage prediction of protein ?-sheets by neural networks, alignments and graph algorithms , 2005, ISMB.
[10] B. Rost,et al. Prediction of protein secondary structure at better than 70% accuracy. , 1993, Journal of molecular biology.
[11] Gianluca Pollastri,et al. Combining protein secondary structure prediction models with ensemble methods of optimal complexity , 2004, Neurocomputing.
[12] Lawrence R. Rabiner,et al. A tutorial on hidden Markov models and selected applications in speech recognition , 1989, Proc. IEEE.
[13] BaldiPierre,et al. Three-stage prediction of protein β-sheets by neural networks, alignments and graph algorithms , 2005 .
[14] Ronald M. Levy,et al. Iterative sequence/secondary structure search for protein homologs: comparison with amino acid sequence alignments and application to fold recognition in genome databases , 2000, Bioinform..
[15] F. Young. Biochemistry , 1955, The Indian Medical Gazette.
[16] Giovanni Soda,et al. Exploiting the past and the future in protein secondary structure prediction , 1999, Bioinform..
[17] Silvio C. E. Tosatto,et al. The SSEA server for protein secondary structure alignment , 2005, Bioinform..
[18] María S. Pérez-Hernández,et al. Bayesian network multi-classifiers for protein secondary structure prediction , 2004, Artif. Intell. Medicine.
[19] Guy M. McKhann,et al. Biochemistry. 3rd edition , 1988, The Yale Journal of Biology and Medicine.
[20] Wei Chu,et al. Bayesian segmental models with multiple sequence alignment profiles for protein secondary structure and contact map prediction , 2006, IEEE/ACM Transactions on Computational Biology and Bioinformatics.
[21] Piero Fariselli,et al. A new decoding algorithm for hidden Markov models improves the prediction of the topology of all-beta membrane proteins , 2005, BMC Bioinformatics.
[22] Temple F. Smith,et al. Protein fold recognition by total alignment probability , 2000, Proteins.
[23] Yücel Altunbasak,et al. Protein secondary structure prediction for a single-sequence using hidden semi-Markov models , 2006, BMC Bioinformatics.
[24] Jean Garnier,et al. FORESST: fold recognition from secondary structure predictions of proteins , 1999, Bioinform..
[25] M. Sternberg,et al. Enhanced genome annotation using structural profiles in the program 3D-PSSM. , 2000, Journal of molecular biology.
[26] Richard A Friesner,et al. A novel fold recognition method using composite predicted secondary structures , 2002, Proteins.
[27] Jacob Goldberger,et al. Sequentially finding the N-Best List in Hidden Markov Models , 2001, IJCAI.
[28] Burkhard Rost,et al. Rising Accuracy of Protein Secondary Structure Prediction , 2003 .
[29] Douglas L. Brutlag,et al. Bayesian Segmentation of Protein Secondary Structure , 2000, J. Comput. Biol..
[30] Nils J. Nilsson,et al. Problem-solving methods in artificial intelligence , 1971, McGraw-Hill computer science series.
[31] Wynne Hsu,et al. Remote homolog detection using local sequence–structure correlations , 2004, Proteins.
[32] Stavros J. Hamodrakas,et al. A Hidden Markov Model method, capable of predicting and discriminating β-barrel outer membrane proteins , 2004, BMC Bioinformatics.
[33] B. Rost,et al. Redefining the goals of protein secondary structure prediction. , 1994, Journal of molecular biology.
[34] Douglas B. Paul. An Efficient A* Stack Decoder Algorithm for Continuous Speech Recognition with a Stochastic Language Model , 1992, HLT.
[35] Frank K. Soong,et al. A Tree.Trellis Based Fast Search for Finding the N Best Sentence Hypotheses in Continuous Speech Recognition , 1990, HLT.
[36] B. Rost,et al. A modified definition of Sov, a segment‐based measure for protein secondary structure prediction assessment , 1999, Proteins.
[37] P. Argos,et al. Seventy‐five percent accuracy in protein secondary structure prediction , 1997, Proteins.
[38] L. Mirny,et al. Protein structure prediction by threading. Why it works and why it does not. , 1998, Journal of molecular biology.
[39] R. Schwartz,et al. A comparison of several approximate algorithms for finding multiple (N-best) sentence hypotheses , 1991, [Proceedings] ICASSP 91: 1991 International Conference on Acoustics, Speech, and Signal Processing.
[40] Lalit R. Bahl,et al. A tree search strategy for large-vocabulary continuous speech recognition , 1995, 1995 International Conference on Acoustics, Speech, and Signal Processing.
[41] Douglas L. Brutlag,et al. Bayesian Protein Structure Prediction , 2002 .
[42] Anders Krogh,et al. Two Methods for Improving Performance of a HMM and their Application for Gene Finding , 1997, ISMB.
[43] R. Schwartz,et al. The N-best algorithms: an efficient and exact procedure for finding the N most likely sentence hypotheses , 1990, International Conference on Acoustics, Speech, and Signal Processing.
[44] Wei Chu,et al. A graphical model for protein secondary structure prediction , 2004, ICML.
[45] Kai Wang,et al. FSSA: a novel method for identifying functional signatures from structural alignments , 2005, Bioinform..
[46] G J Barton,et al. Evaluation and improvement of multiple sequence methods for protein secondary structure prediction , 1999, Proteins.
[47] P. Argos,et al. Incorporation of non-local interactions in protein secondary structure prediction from the amino acid sequence. , 1996, Protein engineering.
[48] W. Miller,et al. A time-efficient, linear-space local similarity algorithm , 1991 .
[49] Richard Bonneau,et al. Distributions of beta sheets in proteins with application to structure prediction , 2002, Proteins.