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  1. NeBcon: protein contact map prediction using neural network training coupled with naïve Bayes classifiers.
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    Journal: Bioinformatics. 2017 Aug 01;33(15)
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  2. Proteus: a random forest classifier to predict disorder-to-order transitioning binding regions in intrinsically disordered proteins.
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    Journal: J Comput Aided Mol Des. 2017 May;31(5)
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  3. InterPred: A pipeline to identify and model protein-protein interactions.
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    Journal: Proteins. 2017 Jun;85(6)
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  4. Improving prediction of helix-helix packing in membrane proteins using predicted contact numbers as restraints.
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    Journal: Proteins. 2017 Jul;85(7)
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  5. VoroMQA: Assessment of protein structure quality using interatomic contact areas.
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    Journal: Proteins. 2017 Jun;85(6)
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  6. Princeton_TIGRESS 2.0: High refinement consistency and net gains through support vector machines and molecular dynamics in double-blind predictions during the CASP11 experiment.
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    Journal: Proteins. 2017 Jun;85(6)
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  7. Correlated mutations select misfolded from properly folded proteins.
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    Journal: Bioinformatics. 2017 May 15;33(10)
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  8. Comparing co-evolution methods and their application to template-free protein structure prediction.
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    Journal: Bioinformatics. 2017 Feb 01;33(3)
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  9. Prediction of Local Quality of Protein Structure Models Considering Spatial Neighbors in Graphical Models.
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    Journal: Sci Rep. 2017 Jan 11;7
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  10. Balancing exploration and exploitation in population-based sampling improves fragment-based de novo protein structure prediction.
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    Journal: Proteins. 2017 May;85(5)
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