[26]Altschul S.F., Koonin E.V. Iterated profile searches with PSI-BLAST — A tool for discovery in protein databases. Trends Biochem Sci, 1998, 23(11): 444–447.
[27]Remmert M., Biegert A., Hauser A., Söding J. HHblits: lightning-fast iterative protein sequence searching by HMM-HMM alignment. Nat Meth, 2012, 9(2): 173.
[28]Tanaka S., Scheraga H.A. Medium- and long-range interaction parameters between amino acids for predicting three-dimensional structures of proteins. Macromolecules, 1976, 9(6): 945–950.
[29]Miyazawa S., Jernigan R.L. Estimation of effective interresidue contact energies from protein crystal structures: Quasi-chemical approximation. Macromolecules, 1985, 18(3): 534–552.
[30]Miyazawa S., Jernigan R.L. Residue–residue potentials with a favorable contact pair term and an unfavorable high packing density term, for simulation and threading. J Mol Biol, 1996, 256(3): 623–644.
[31]Skolnick J., Godzik A., Jaroszewski L., Kolinski A. Derivation and testing of pair potentials for protein folding. When is the quasichemical approximation correct? Prot Sci, 1997, 6(3): 676–688.
[32]Simmons, K.T., Ingo R., Charles K., A. F.B., Chris B., David B. Improved recognition of nativelike protein structures using a combination of sequence-dependent and sequence-independent features of proteins. Proteins: Structure, Function, and Bioinformatics, 1999, 34(1): 82–95.
[33]Zhang C., Kim S.-H. Environment-dependent residue contact energies for proteins. Proc Natl Acad Sci USA, 2000, 97(6): 2550–2555.
[34]Cristian M., Flavio S., R. B.J., Amos M. Learning effective amino acid interactions through iterative stochastic techniques. Proteins, 2001, 42(3): 422–431.
[35]Riis S.K., Krogh A. Improving prediction of protein secondary structure using structured neural networks and multiple sequence alignments. J Comput Biol, 1996, 3(1): 163–183.
[36]Jagla B., Schuchhardt J. Adaptive encoding neural networks for the recognition of human signal peptide cleavage sites. Bioinformatics, 2000, 16(3): 245–250.
[37]Meiler J., Müller M., Zeidler A., Schmäschke F. Generation and evaluation of dimension-reduced amino acid parameter representations by artificial neural networks. Mol Model Annu, 2001, 7(9): 360–369.
[38]Xu Y., Song J., Wilson C., Whisstock J.C. PhosContext2vec: A distributed representation of residue-level sequence contexts and its application to general and kinase-specific phosphorylation site prediction. Sci Rep, 2018, 8.
[39]Yang K.K., Wu Z., Bedbrook C.N., Arnold F.H. Learned protein embeddings for machine learning. Bioinformatics, 2018, 34(15): 2642–2648.
[40]Mikolov T., Sutskever I., Chen K., Corrado G.S., Dean J. Distributed representations of words and phrases and their compositionality. Proceedings of the 26th International Conference on Neural Information Processing Systems, Curran Associates inc., New York, USA, 2013, 3111–3119.
[41]Hou J., Adhikari B., Cheng J. DeepSF: Deep convolutional neural network for mapping protein sequences to folds. Bioinformatics, 2017, 34(8): 1295–1303.
[42]Heffernan R., Yang Y., Paliwal K., Zhou Y. Capturing non-local interactions by long short term memory bidirectional recurrent neural networks for improving prediction of protein secondary structure, backbone angles, contact numbers, and solvent accessibility. Bioinformatics, 2017: btx218.
[43]Anfinsen C.B. Principles that govern the folding of protein chains. Science, 1973, 181(4096): 223–230.
[44]Chen J., Guo M., Wang X., Liu B. A comprehensive review and comparison of different computational methods for protein remote homology detection. Brief Bioinfo, 2018, 19(2): 231–244.
[45]David R. Applications of nonlinear system identification to protein structural prediction. Thesis (S.M.) — Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2000.
[46]Zhong W., Altun G., Tian X., Harrison R., Tai P.C., Pan Y. Parallel protein secondary structure prediction based on neural networks. IEEE, 2004: 2968–2971.
[47]Dongardive J., Abraham S. Reaching optimized parameter set: Protein secondary structure prediction using neural network. Neural Comput Appl, 2017, 28(8): 1947–1974.
[48]Yang Y., Gao J., Wang J., Heffernan R., Hanson J., Paliwal K., Zhou Y. Sixty-five years of the long march in protein secondary structure prediction: The final stretch? Brief Bioinfo, 2016: bbw129.
[49]Asgari E., McHardy A.C., Mofrad M.R. Probabilistic variable-length segmentation of protein sequences for discriminative motif discovery (DiMotif) and sequence embedding (ProtVecX). Sci Rep, 2019, 9(1): 3577.
[50]Consortium U. UniProt: A hub for protein information. Nucleic Acids Res, 2014, 43(D1): D204–D212.
[51]Li Z., Yu Y. Protein secondary structure prediction using cascaded convolutional and recurrent neural networks. arXiv preprint arXiv:1604.07176, 2016.
[52]Wang G., Dunbrack R.L. PISCES: A protein sequence culling server. Bioinformatics, 2003, 19(12): 1589–1591.
[53]Cuff J.A. and Barton G.J. Evaluation and improvement of multiple sequence methods for protein secondary structure prediction. Proteins, 1999, 34(4): 508–519.
[54]John M., Krzysztof F., Andriy K., Torsten S., Anna T. Critical assessment of methods of protein structure prediction (CASP) — Round x. Proteins, 2013, 82(S2): 1–6.
[55]Kinch L.N., Li W., Schaeffer R.D., Dunbrack R.L., Monastyrskyy B., Kryshtafovych A., Grishin N.V. CASP 11 target classification. Proteins, 2016, 84(S1): 20–33.
[56]Moult J., Fidelis K., Kryshtafovych A., Schwede T., Tramontano A. Critical assessment of methods of protein structure prediction (CASP) — Round XII. Proteins, 2018, 86: 7–15.
[57]Wolfgang K., Christian S. Dictionary of protein secondary structure: Pattern recognition of hydrogen-bonded and geometrical features. Biopolymers, 2004, 22(12): 2577–2637.
[58]Pedregosa F., Varoquaux G., Gramfort A., Michel V., Thirion B., Grisel O., Blondel M., Prettenhofer P., Weiss R., Dubourg V. Scikitlearn: Machine learning in Python. J Machine Learning Res, 2011, 12(Oct): 2825–2830.
[59]Abadi M.N., Barham P., Chen J., Chen Z., Davis A., Dean J., Devin M., Ghemawat S., Irving G., Isard M. Tensorflow: A system for large-scale machine learning. In 12th {USENIX} Symposium on Operating Systems Design and Implementation ({OSDI} 16), 2016, pp. 265–283.
[60]Chen J., Guo M., Wang X., Liu B. A comprehensive review and comparison of different computational methods for protein remote homology detection. Brief Bioinfo, 2016, 19(2): 231–244.
[61]Hou J., Adhikari B., Cheng J. DeepSF: Deep convolutional neural network for mapping protein sequences to folds. Bioinformatics, 2017, 34(8): 1295–1303.
[62]Xia J., Peng Z., Qi D., Mu H., Yang J. An ensemble approach to protein fold classification by integration of template-based assignment and support vector machine classifier. Bioinformatics, 2016, 33(6): 863–870.
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