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Items where Author is "Zhou, Y." |
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Group by: Item Type | No Grouping Jump to: Book Chapter | Journal Article Number of items: 6. Book ChapterYang, Y. and Heffernan, R. and Paliwal, K. and Lyons, J. and Dehzangi, A. and Sharma, Alokanand and Wang, J. and Sattar, A. and Zhou, Y. (2017) SPIDER2: a package to predict secondary structure, accessible surface area, and main - chain torsional angles by deep neural networks. In: Prediction of Protein Secondary Structure. Methods in Molecular Biology. Humana Press, New York, pp. 55-63. ISBN 9781493964048 Journal ArticleHeffernan, R. and Dehzangi, Abdollah and Lyons, J. and Paliwal, K.K. and Sharma, Alokanand and Wang, J. and Sattar, A. and Zhou, Y. and Yang, Y. (2016) Highly Accurate Sequence-based Prediction of Half-Sphere Exposures of Amino Acid Residues in Proteins. Bioinformatics, 32 (6). pp. 843-849. ISSN 1367-4803 Lyons, J. and Dehzangi, A. and Heffernan, R. and Yang, Yuedong and Zhou, Y. and Sharma, Alokanand and Paliwal, K.K. (2015) Advancing the accuracy of protein fold recognition by utilizing profiles from hidden Markov models. IEEE Transactions on Nanobioscience, 14 (7). pp. 761-772. ISSN 1536-1241 Heffernan, R. and Dehzangi, A. and Lyons, J. and Paliwal, K.K. and Sharma, Alokanand and Wang, J. and Sattar, A. and Zhou, Y. and Yang, Y. (2015) Highly accurate sequence - based prediction of half - sphere exposures of amino acid residues in proteins. Bioinformatics, 14 . pp. 1-7. ISSN 1367-4803 Heffernan, R. and Paliwal, K.K. and Lyons, J. and Dehzangi, A. and Sharma, Alokanand and Wang, J. and Sattar, A. and Yang, Y. and Zhou, Y. (2015) Improving prediction of secondary structure, local backbone angles, and solvent accessible surface area of proteins by iterative deep learning. Scientific Reports, 5 (11476). pp. 1-11. ISSN NA Lyons, S. and Dehzangi, A. and Heffernan, R. and Sharma, Alokanand and Paliwal, K.K. and Sattar, A. and Zhou, Y. and Yang, Y. (2014) Predicting backbone Cα angles and dihedrals from protein sequences by stacked sparse auto-encoder deep neural network. Journal of Computational Chemistry, 35 (28). pp. 2040-2046. ISSN 0192-8651 |