Biomedical Data Mining for Information Retrieval. Группа авторов
Чтение книги онлайн.
Читать онлайн книгу Biomedical Data Mining for Information Retrieval - Группа авторов страница 25
30. Huang, Q., Zhang, P., Wu, D., Zhang, L., Turbo Learning for CaptionBot and DrawingBot, in: Advances in Neural Information Processing Systems, vol. 20, pp. 6456–6466, Curran Associates Inc., USA, 2018.
31. Xu, T. et al., AttnGAN: Fine-Grained Text to Image Generation with Attentional Generative Adversarial Networks, in: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018.
32. Kosylo, N. et al., Artificial Intelligence on Job-Hopping Forecasting: AI on Job-Hopping, in: Portland International Conference on Management of Engineering and Technology (PICMET), 2018.
33. Keasar, C. et al., An analysis and evaluation of the WeFold collaborative for protein structure prediction and its pipelines in CASP11 and CASP12. Sci. Rep., 8, 1, 9939, 2018.
34. Hou, J., Wu, T., Cao, R., Cheng, J., Protein tertiary structure modeling driven by deep learning and contact distance prediction in CASP13. bioRxiv, Open Access 552–422, 15 April 2019, https://doi.org/10.1002/prot.25697.
35. Pauling, L. and Corey, R.B., The pleated sheet, a new layer configuration of the polypeptide chain. Proc. Natl. Acad. Sci., 37, 251–256, 1951.
36. Pauling, L., Corey, R.B., Branson, H.R., The structure of proteins: Two hydrogen bonded helical configurations of the polypeptide chain. Proc. Natl. Acad. Sci., 37, 205–211, 1951.
37. Kendrew, J.C., Dickerson, R.E., Strandberg, B.E., Hart, R.J., Davies, D.R., Phillips, D.C., Shore, V.C., Structure of myoglobin: A three-dimensional Fourier synthesis at 2_a resolution. Nature, 185, 422–427, 1960.
38. Perutz, M.F., Rossmann, M.G., Cullis, A.F., Muirhead, G., Will, G., North, A.T., Structure of haemoglobin: A three-dimensional Fourier synthesis at 5.5 Angstrom resolution, obtained by x-ray analysis. Nature, 185, 416–422, 1960.
39. Dill, K.A., Dominant forces in protein folding. Biochemistry, 31, 7134–7155, 1990.
40. Laskowski, R.A., Watson, J.D., Thornton, J.M., From protein structure to biochemical function? J. Struct. Funct. Genomics, 4, 167–177, 2003.
41. Travers, DNA conformation and protein binding. Annu. Rev. Biochem., 58, 427–452, 1989.
42. Bjorkman, P.J. and Parham, P., Structure, function and diversity of class I major histocompatibility complex molecules. Annu. Rev. Biochem., 59, 253– 288, 1990.
43. Yang, J., Cao, R., Si, D., EMNets: A Convolutional Autoencoder for Protein Surface Retrieval Based on Cryo-Electron Microscopy Imaging, in: Proceedings of the 2018 ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics—BCB ‘18, Washington, DC, USA, pp. 639–644, 2018.
44. Ng, A. and Si, D., Beta-Barrel Detection for Medium Resolution CryoElectron Microscopy Density Maps Using Genetic Algorithms and Ray Tracing. J. Comput. Biol., 25, 3, 326–336, Mar. 2018.
45. Li, R., Si, D., Zeng, T., Ji, S., He, J., Deep Convolutional Neural Networks for Detecting Secondary Structures in Protein Density Maps from Cryo-Electron Microscopy. Proceedings, 2016, 41–46, Dec. 2016.
46. Si, D., Ji, S., Nasr, K.A., He, J., A machine learning approach for the identification of protein secondary structure elements from electron cryo-microscopy density maps. Biopolymers, 97, 9, 698–708, Sep. 2012.
47. Huang, Q., Zhang, P., Wu, D., Zhang, L., Turbo Learning for CaptionBot and DrawingBot, in: Advances in Neural Information Processing Systems, vol. 31, S. Bengio, H. Wallach, H. Larochelle, K. Grauman, N. Cesa-Bianchi, R. Garnett (Eds.), pp. 6456–6466, Curran Associates, Inc., USA, 2018.
48. Xu, T. et al., AttnGAN: Fine-Grained Text to Image Generation with Attentional Generative Adversarial Networks, in: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018.
49. Kosylo, N. et al., Artificial Intelligence on Job-Hopping Forecasting: AI on Job-Hopping, in: 2018 Portland International Conference on Management of Engineering and Technology (PICMET), 2018.
50. Keasar, C. et al., An analysis and evaluation of the WeFold collaborative for protein structure prediction and its pipelines in CASP11 and CASP12. Sci. Rep., 8, 1, 9939, Jul. 2018.
51. Hou, J., Wu, T., Cao, R., Cheng, J., Protein tertiary structure modeling driven by deep learning and contact distance prediction in CASP13. bioRxiv, Open Access 552422, 15 April 2019, https://doi.org/10.1002/prot.25697.
52. Moult, J., Fidelis, K., Kryshtafovych, A., Schwede, T., Tramontano, A., Critical assessment of methods of protein structure prediction (CASP)-Round XII. Proteins, 86, Suppl 1, 7–15, Mar. 2018.
53. Kendrew, J.C., Dickerson, R.E., Strandberg, B.E., Hart, R.J., Davies, D.R., Phillips, D.C., Shore, V.C., Structure of myoglobin: A three-dimensional Fourier synthesis at 2_a resolution. Nature, 185, 422–427, 1960.
54. Perutz, M.F., Rossmann, M.G., Cullis, A.F., Muirhead, G., Will, G., North, A.T., Structure of haemoglobin: A three-dimensional Fourier synthesis at 5.5 Angstrom resolution, obtained by X-ray analysis. Nature, 185, 416–422, 1960.
55. Travers, A., DNA conformation and protein binding. Annu. Rev. Biochem., 58, 427–452, 1989.
56. Bjorkman, P.J. and Parham, P., Structure, function and diversity of class I major histocompatibility complex molecules. Annu. Rev. Biochem., 59, 253– 288, 1990.
57. Bernstein, F.C., Koetzle, T.F., Williams, G.J., Meyer, E.F., Brice, M.D., Rodgers, J.R., Kennard, O., Shimanouchi, T., Tasumi, M., The protein data bank. Eur. J. Biochem., 80, 319–324, 1977. [CrossRef] [PubMed].
58. Consortium, U., The universal protein resource (UniProt). Nucleic Acids Res., 36, D190–D195, 2008. [CrossRef] [PubMed].
59. Kabsch, W. and Sander, C., Dictionary of protein secondary structure: Pattern recognition of hydrogen-bonded and geometrical features. Biopolymers, 22, 2577–2637, 1983. [CrossRef] [PubMed].
60. Murzin, A.G., Brenner, S.E., Hubbard, T., Chothia, C., Scop: A structural classification of proteins database for the investigation of sequences and structures. J. Mol. Biol., 247, 536–540, 1995. [CrossRef].
61. Andreeva, A., Howorth, D., Chothia, C., Kulesha, E., Murzin, A.G., SCOP2 prototype: A new approach to protein structure mining. Nucleic Acids Res., 42, 310–314, 2014. [CrossRef] [PubMed].
62. Sillitoe, I., Lewis, T.E., Cuff, A., Das, S., Ashford, P., Dawson, N.L., Furnham, N., Laskowski, R.A., Lee, D., Lees, J.G., Cath: Comprehensive structural and functional annotations for genome sequences. Nucleic Acids Res., 43, 376– 381, 2015.
63. Karplus, K., Barrett, C., Hughey, R., Hidden Markov models for detecting remote protein homologies. Bioinfo., 14, 10, 846–856, 1998.
64. Eddy, S.R., Profile hidden Markov models. Bioinfo., 14, 755–763, 1998.
65. Soeding, J., Protein homology detection by HMM–HMM comparison. Bioinfo., 21, 951–960, 2005.
66.