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» A graphical model for protein secondary structure prediction
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BMCBI
2007
113views more  BMCBI 2007»
13 years 7 months ago
Learning biophysically-motivated parameters for alpha helix prediction
Background: Our goal is to develop a state-of-the-art protein secondary structure predictor, with an intuitive and biophysically-motivated energy model. We treat structure predict...
Blaise Gassend, Charles W. O'Donnell, William Thie...
CBMS
2006
IEEE
14 years 1 months ago
JSSPrediction: a Framework to Predict Protein Secondary Structures Using Integration
Identifying protein secondary structures is a difficult task. Recently, a lot of software tools for protein secondary structures prediction have been produced and made available ...
Luigi Palopoli, Simona E. Rombo, Giorgio Terracina...
BMCBI
2006
137views more  BMCBI 2006»
13 years 7 months ago
Improving the accuracy of protein secondary structure prediction using structural alignment
Background: The accuracy of protein secondary structure prediction has steadily improved over the past 30 years. Now many secondary structure prediction methods routinely achieve ...
Scott Montgomerie, Shan Sundararaj, Warren J. Gall...
CDES
2006
97views Hardware» more  CDES 2006»
13 years 8 months ago
Protein Secondary Structure Prediction Accuracy versus Reduction Methods
Predicting protein secondary structure is a key step in determining the 3D structure of a protein that determines its function. The Dictionary of Secondary Structure of Proteins (...
Saad Osman Abdalla Subair, Safaai Deris
ICML
2004
IEEE
14 years 7 months ago
Kernel conditional random fields: representation and clique selection
Kernel conditional random fields (KCRFs) are introduced as a framework for discriminative modeling of graph-structured data. A representer theorem for conditional graphical models...
John D. Lafferty, Xiaojin Zhu, Yan Liu