Learning in graphical models
Predicting the &bgr;-helix fold from protein sequence data
RECOMB '01 Proceedings of the fifth annual international conference on Computational biology
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Maximum Entropy Markov Models for Information Extraction and Segmentation
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Protein Secondary-Structure Modeling with Probabilistic Networks
Proceedings of the 1st International Conference on Intelligent Systems for Molecular Biology
Table extraction using conditional random fields
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
RECOMB '04 Proceedings of the eighth annual international conference on Resaerch in computational molecular biology
A graphical model for protein secondary structure prediction
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Shallow parsing with conditional random fields
NAACL '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - Volume 1
Bioinformatics
Protein quaternary fold recognition using conditional graphical models
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
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
Expansion finding for given acronyms using conditional random fields
WAIM'11 Proceedings of the 12th international conference on Web-age information management
Finding the game flow from sports video
J-MRE '11 Proceedings of the 2011 joint ACM workshop on Modeling and representing events
Variational conditional random fields for online speaker detection and tracking
Speech Communication
PROBABILISTIC MODELS FOR FOCUSED WEB CRAWLING
Computational Intelligence
Remote homology detection on alpha-structural proteins using simulated evolution
Proceedings of the ACM Conference on Bioinformatics, Computational Biology and Biomedicine
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Protein fold recognition is an important step towards understanding protein three-dimensional structures and their functions. A conditional graphical model, i.e. segmentation conditional random fields (SCRFs), is proposed to solve the problem. In contrast to traditional graphical models such as hidden markov model (HMM), SCRFs follow a discriminative approach. It has the flexibility to include overlapping or long-range interaction features over the whole sequence, as well as global optimally solutions for the parameters. On the other hand, the segmentation setting in SCRFs makes its graphical structures intuitively similar to the protein 3-D structures and more importantly, provides a framework to model the long-range interactions directly. Our model is applied to predict the parallel β-helix fold, an important fold in bacterial infection of plants and binding of antigens. The cross-family validation shows that SCRFs not only can score all known β-helices higher than non β-helices in Protein Data Bank, but also demonstrate more success in locating each rung in the known β-helix proteins than BetaWrap, a state-of-the-art algorithm for predicting β-helix fold, and HMMER, a general motif detection algorithm based on HMM. Applying our prediction model to Uniprot database, we hypothesize previously unknown β-helices.