Bayesian Segmental Models with Multiple Sequence Alignment Profiles for Protein Secondary Structure and Contact Map Prediction Export

@article{citeulike:3979862, abstract = {In this paper, we develop a segmental semi-Markov model (SSMM) for protein secondary structure prediction which incorporates multiple sequence alignment profiles with the purpose of improving the predictive performance. The segmental model is a generalization of the hidden Markov model where a hidden state generates segments of various length and secondary structure type. A novel parameterized model is proposed for the likelihood function that explicitly represents multiple sequence alignment profiles to capture the segmental conformation. Numerical results on benchmark data sets show that incorporating the profiles results in substantial improvements and the generalization performance is promising. By incorporating the information from long range interactions in \beta{\hbox{-}}{\rm sheets}, this model is also capable of carrying out inference on contact maps. This is an important advantage of probabilistic generative models over the traditional discriminative approach to protein secondary structure prediction. The Web server of our algorithm and supplementary materials are available at http://public.kgi.edu/\~{}wild/bsm.html.}, address = {Los Alamitos, CA, USA}, author = {Chu, Wei and Ghahramani, Zoubin and Podtelezhnikov, Alexei and Wild, David L.}, citeulike-article-id = {3979862}, citeulike-linkout-0 = {http://doi.ieeecomputersociety.org/10.1109/TCBB.2006.17}, citeulike-linkout-1 = {http://dx.doi.org/10.1109/TCBB.2006.17}, comment = {protein}, doi = {10.1109/TCBB.2006.17}, issn = {1545-5963}, journal = {IEEE/ACM Transactions on Computational Biology and Bioinformatics}, keywords = {contactmap, proteomics, secondary-structure}, number = {2}, pages = {98--113}, posted-at = {2009-01-29 21:36:06}, priority = {4}, publisher = {IEEE Computer Society}, title = {Bayesian Segmental Models with Multiple Sequence Alignment Profiles for Protein Secondary Structure and Contact Map Prediction}, url = {http://dx.doi.org/10.1109/TCBB.2006.17}, volume = {3}, year = {2006} }

TY - JOUR ID - citeulike:3979862 L3 - citeulike-article-id:3979862 N2 - In this paper, we develop a segmental semi-Markov model (SSMM) for protein secondary structure prediction which incorporates multiple sequence alignment profiles with the purpose of improving the predictive performance. The segmental model is a generalization of the hidden Markov model where a hidden state generates segments of various length and secondary structure type. A novel parameterized model is proposed for the likelihood function that explicitly represents multiple sequence alignment profiles to capture the segmental conformation. Numerical results on benchmark data sets show that incorporating the profiles results in substantial improvements and the generalization performance is promising. By incorporating the information from long range interactions in \beta{\hbox{-}}{\rm sheets}, this model is also capable of carrying out inference on contact maps. This is an important advantage of probabilistic generative models over the traditional discriminative approach to protein secondary structure prediction. The Web server of our algorithm and supplementary materials are available at http://public.kgi.edu/~wild/bsm.html. IS - 2 JF - IEEE/ACM Transactions on Computational Biology and Bioinformatics SN - 1545-5963 AD - Los Alamitos, CA, USA EP - 113 TI - Bayesian Segmental Models with Multiple Sequence Alignment Profiles for Protein Secondary Structure and Contact Map Prediction PB - IEEE Computer Society VL - 3 SP - 98 KW - contactmap KW - proteomics KW - secondary-structure N1 - protein AU - Chu, Wei AU - Ghahramani, Zoubin AU - Podtelezhnikov, Alexei AU - Wild, David PY - 2006/// UR - http://dx.doi.org/10.1109/TCBB.2006.17 ER -