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  1. RFQAmodel: Random Forest Quality Assessment to identify a predicted protein structure in the correct fold.
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    Journal: PLoS One. 2019;14(10)
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  2. Protein structure prediction using multiple deep neural networks in the 13th Critical Assessment of Protein Structure Prediction (CASP13).
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    Journal: Proteins. 2019 Dec;87(12)
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  3. Recent developments in deep learning applied to protein structure prediction.
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    Journal: Proteins. 2019 Dec;87(12)
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  4. Introducing "best single template" models as reference baseline for the Continuous Automated Model Evaluation (CAMEO).
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    Journal: Proteins. 2019 Dec;87(12)
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  5. Integrative Protein Modeling in RosettaNMR from Sparse Paramagnetic Restraints.
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    Journal: Structure. 2019 Nov 05;27(11)
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  6. Effective hybrid approach for protein structure prediction in a two-dimensional Hydrophobic-Polar model.
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    Journal: Comput Biol Med. 2019 Oct;113
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  7. Analysis of distance-based protein structure prediction by deep learning in CASP13.
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    Journal: Proteins. 2019 Dec;87(12)
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  8. Getting to Know Your Neighbor: Protein Structure Prediction Comes of Age with Contextual Machine Learning.
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    Journal: J Comput Biol. 2020 May;27(5)
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  9. PaleAle 5.0: prediction of protein relative solvent accessibility by deep learning.
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    Journal: Amino Acids. 2019 Sep;51(9)
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  10. Assessment of protein assembly prediction in CASP13.
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    Journal: Proteins. 2019 Dec;87(12)
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