Protein Secondary-Structure Modeling with Probabilistic Networks
Proceedings of the 1st International Conference on Intelligent Systems for Molecular Biology
Statistical models and monte carlo methods for protein structure prediction
Statistical models and monte carlo methods for protein structure prediction
Predicting protein folds with structural repeats using a chain graph model
ICML '05 Proceedings of the 22nd international conference on Machine learning
Enhanced max margin learning on multimodal data mining in a multimedia database
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Free energy estimates of all-atom protein structures using generalized belief propagation
RECOMB'07 Proceedings of the 11th annual international conference on Research in computational molecular biology
Conditional graphical models for protein structure prediction
Conditional graphical models for protein structure prediction
Adaptive Exact Inference in Graphical Models
The Journal of Machine Learning Research
Segmentation conditional random fields (SCRFs): a new approach for protein fold recognition
RECOMB'05 Proceedings of the 9th Annual international conference on Research in Computational Molecular Biology
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In this paper, we present a graphical model for protein secondary structure prediction. This model extends segmental semi-Markov models (SSMM) to exploit multiple sequence alignment profiles which contain information from evolutionarily related sequences. A novel parameterized model is proposed as the likelihood function for the SSMM to capture the segmental conformation. By incorporating the information from long range interactions in ß-sheets, this model is capable of carrying out inference on contact maps. The numerical results on benchmark data sets show that incorporating the profiles results in substantial improvements and the generalization performance is promising.