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  1. Balancing multiple objectives in conformation sampling to control decoy diversity in template-free protein structure prediction.
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    Journal: BMC Bioinformatics. 2019 Apr 25;20(1)
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  2. End-to-End Differentiable Learning of Protein Structure.
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    Journal: Cell Syst. 2019 Apr 24;8(4)
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  3. Protein tertiary structure modeling driven by deep learning and contact distance prediction in CASP13.
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    Journal: Proteins. 2019 Dec;87(12)
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  4. DESTINI: A deep-learning approach to contact-driven protein structure prediction.
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    Journal: Sci Rep. 2019 Mar 05;9(1)
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  5. Graph-Based Community Detection for Decoy Selection in Template-Free Protein Structure Prediction.
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    Journal: Molecules. 2019 Feb 28;24(5)
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  6. Protein structure prediction from inaccurate and sparse NMR data using an enhanced genetic algorithm.
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    Journal: Comput Biol Chem. 2019 Apr;79
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  7. Improving prediction of protein secondary structure, backbone angles, solvent accessibility and contact numbers by using predicted contact maps and an ensemble of recurrent and residual convolutional neural networks.
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    Journal: Bioinformatics. 2019 Jul 15;35(14)
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  8. BetaDL: A protein beta-sheet predictor utilizing a deep learning model and independent set solution.
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    Journal: Comput Biol Med. 2019 Jan;104
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  9. Two New Heuristic Methods for Protein Model Quality Assessment.
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    Journal: IEEE/ACM Trans Comput Biol Bioinform. 2020;17(4)
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  10. Single-sequence-based prediction of protein secondary structures and solvent accessibility by deep whole-sequence learning.
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    Journal: J Comput Chem. 2018 Oct 05;39(26)
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