Continuous Conditional Random Fields for Regression in Remote Sensing
Proceedings of the 2010 conference on ECAI 2010: 19th European Conference on Artificial Intelligence
Conditional graphical models for protein structure prediction
Conditional graphical models for protein structure prediction
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
Collaborative discovery through biological language modeling interface
Ambient Intelligence in Everyday Life
Computational biology and language
Ambient Intelligence for Scientific Discovery
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Motivation: Protein secondary structure prediction is an important step towards understanding how proteins fold in three dimensions. Recent analysis by information theory indicates that the correlation between neighboring secondary structures are much stronger than that of neighboring amino acids. In this article, we focus on the combination problem for sequences, i.e. combining the scores or assignments from single or multiple prediction systems under the constraint of a whole sequence, as a target for improvement in protein secondary structure prediction. Results: We apply several graphical chain models to solve the combination problem and show that they are consistently more effective than the traditional window-based methods. In particular, conditional random fields (CRFs) moderately improve the predictions for helices and, more importantly, for beta sheets, which are the major bottleneck for protein secondary structure prediction.