Biomedical Data Mining for Information Retrieval

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This book comprehensively covers the topic of mining biomedical text, images and visual features towards information retrieval. Biomedical and Health Informatics is an emerging field of research at the intersection of information science, computer science, and health care and brings tremendous opportunities and challenges due to easily available and abundant biomedical data for further analysis. The aim of healthcare informatics is to ensure the high-quality, efficient healthcare, better treatment and quality of life by analyzing biomedical and healthcare data including patient's data, electronic health records (EHRs) and lifestyle. Previously it was a common requirement to have a domain expert to develop a model for biomedical or healthcare; however, recent advancements in representation learning algorithms allows us to automatically to develop the model. Biomedical Image Mining, a novel research area, due to its large amount of biomedical images increasingly generates and stores digitally. These images are mainly in the form of computed tomography (CT), X-ray, nuclear medicine imaging (PET, SPECT), magnetic resonance imaging (MRI) and ultrasound. Patients' biomedical images can be digitized using data mining techniques and may help in answering several important and critical questions related to health care. Image mining in medicine can help to uncover new relationships between data and reveal new useful information that can be helpful for doctors in treating their patients.

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24. Zou, J., Huss, M., Abid, A., Mohammadi, P., Torkamani, A., Telenti, A., A primer on deep learning in genomics. Nat. Genet. , 51, 12–8, 2019.

25. Eraslan, G., Avsec, Ž., Gagneur, J., Theis, F.J., Deep learning: New computational modelling techniques for genomics. Nat. Rev. Genet. , 20, 389–403, 2019.

26. Yang, J., Cao, R., Si, D., EMNets: A Convolutional Autoencoder is made available under a CC-BY-NC-ND 4.0 International license. bioRxiv , preprint, 2018, https://doi.org/10.1101/561027. The copyright holder for this preprint (which was not peer-reviewed) is the author/funder. It for Protein Surface Retrieval Based on Cryo-Electron Microscopy Imaging,” in Proceedings of the ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics—BCB ‘18 , Washington, DC, USA, pp. 639–644.

27. 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, 6, 326–336, 2018.

28. 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 , pp. 41–46, 2016.

29. 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, 2012.

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].

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