Skip to main content
SpringerLink
Log in

Deep Learning in Gene Expression Modeling

  • Chapter
  • First Online:
Handbook of Deep Learning Applications

Part of the book series: Smart Innovation, Systems and Technologies ((SIST,volume 136))

Abstract

Developing computational intelligence algorithms for learning insights from data has been a growing intellectual challenge. Much advances have already been made through data mining but there is an increasing research focus on deep learning to exploit the massive improvement in computational power. This chapter presents recent advancements in deep learning research and identifies some remaining challenges as drawn from using deep learning in the application area of gene expression modelling. It highlights deep learning (DL) as a branch of Machine Learning (ML), the various models and theoretical foundations, its motivations as to why we need deep learning in the context of evolving Big Data, particularly in the area of gene expression level classification. We present a review, and strengths and weaknesses of various DL models and their computational power to specific to gene expression modeling. Deep learning models are efficient feature selectors and therefore work best in high dimension datasets. We present major research challenges in feature extraction and selection using different deep models. Our case studies are drawn from gene expression datasets. Hence we report some of the key formats of gene expression datasets used for deep learning. As ongoing research we will discuss the future prospects of deep learning for gene expression modelling.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 149.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 199.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. S. Agarwal, G. Pandey, M.D. Tiwari, Data mining in education: data classification and decision tree approach. Int. J. e-Educ. e-Bus. e-Manag. e-Learn. 2(12) (2012)

    Google Scholar 

  2. Y. Li, W. Ma, Applications of artificial neural networks in financial economics: a survey, in International Symposium on Computational Intelligence and Design (ISCID) (2010)

    Google Scholar 

  3. H. Xie, J. Shi, W. Lu, W. Cu, Dynamic Bayesian networks in electronic equipment health diagnosis, in IEEE Prognostics and System Health Management Conference (PHM-Chengdu) (2016)

    Google Scholar 

  4. Y. LeCun, Y. Benjio, G. Hinton, Deep learning. Nature 521(7553), 436–444 (2015)

    Article  Google Scholar 

  5. L. Weifeng, V. Benjamin, X. Liu, Hsinchu, Deep learning: an overview—lecture notes, University of Arizona, Arizona, Apr 2015

    Google Scholar 

  6. Y. LeCun, L. Bottou, Y. Benjio, P. Haffner, Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278–2324 (1998)

    Article  Google Scholar 

  7. T. Raiko, H. Valpola, Y. LeCun, Deep learning made easier by linear transformations in perceptrons, in Proceedings of the 15th International Conference on Artificial Intelligence and Statistics (AISTATS), La Palma, Canary Islands (2012)

    Google Scholar 

  8. Y. Benjio, Scaling up deep learning, in Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, New York, USA (2014)

    Google Scholar 

  9. Y. Bengio, Deep learning of representations for unsupervised and transfer learning, in JMLR: Workshop and Conference Proceedings, vol. 27, pp. 17–37 (2012)

    Google Scholar 

  10. Y. Benjio, Deep learning: theoretical motivations, 3 Aug 2015, http://videolectures.net/deeplearning2015_bengio_theoretical_motivations/. Accessed 26 June 2017

  11. Y. Benjio, Deep learning architectures for AI. Found. Trends Mach. Learn. 2(1), 1–127 (2009)

    Article  Google Scholar 

  12. J. Schmidhuber, Deep learning in neural networks: an overview. Neural Netw. 61, 85–117 (2015)

    Article  Google Scholar 

  13. P.-N. Tan, M. Steinbach, V. Kumar, Introduction to Data Mining (Pearson Education Inc., Boston, 2006)

    Google Scholar 

  14. G. Hinton, R. Salakhutdinov, Reducing the dimensionality of data with neural networks. Science 313, 504–507 (2006)

    Article  MathSciNet  Google Scholar 

  15. M. Ranzato, C. Poultney, S. Chopra, Y. LeCun, Efficient learning of sparse representations with an energy-based model. Adv. Neural. Inf. Process. Syst. 13, 1137–1144 (2006)

    Google Scholar 

  16. M.D. Zeiler, M. Ranzato, R. Monga, M. Mao, K. Yang, Q.V. Le, P. Nguyen, A. Senior, V. Vanhoucke, J. Dean, G.E. Hinton, On rectified linear units for speech procesing, in IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 3517–3521 (2013)

    Google Scholar 

  17. I. Lopez-Moreno, J. Gonzalez-Dominguez, D. Martinez, O. Plchot, J. Gonzalez-Rodriguez, P.J. Moreno, On the use of deep feedforward neural networks for automatic language identification. Comput. Speech Lang. 40, 46–59 (2016)

    Article  Google Scholar 

  18. N.G. Nguyen, V.A. Tran, D.L. Ngo, D. Phan, F.R. Lumbanraja, M.R. Faisal, B. Abapihi, M. Kubo, K. Satou, DNA sequence classification by convolutional neural network. J. Biomed. Sci. Eng. 9, 280–286 (2016)

    Article  Google Scholar 

  19. D.H. Hubel, T.N. Wiesel, Receptive fields and functional architecture of monkey striate cortex. J. Physiol. (1968)

    Google Scholar 

  20. Y. LeCun, J.S. Denker, D. Henderson, R.E. Howard, W. Hubbard, L.D. Jackel, Handwritten digit recognition with a back-propagation algorithm, in Proceedings of NIPS (1990)

    Google Scholar 

  21. A. Krizhevsky, I. Sutskever, G.E. Hinton, Imagenet classification with deep convolutional neural networks, in Proceedings of NIPS (2012)

    Google Scholar 

  22. D.C. Ciresan, U. Meier, J. Masci, L.M. Gambardella, J. Schmidhuber, Flexible, high performance convolutional neural networks for image classification, in Proceedings of IJCAI (2011)

    Google Scholar 

  23. J. Gu, Z. Wang, J. Kuen, L. Ma, A. Sharoudy, B. Shuai, T. Liu, X. Wang, G. Wang, Recent advances in convolutional neural networks (2017). arXiv:1512.07108

  24. M.D. Zeiler, R. Fegus, Visualizing and understanding convolutional networks (2013). arXiv:1311.2901

  25. C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, A. Rabinovich, Going deeper with convolutions, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2015)

    Google Scholar 

  26. K. Simonyan, A. Zisserman, Very deep convolutional networks for large-scale image recognition (2014). arXiv:1409.1556

  27. K. He, X. Zhang, S. Ren, J. Sun, Deep residual learning for image recognition, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2016)

    Google Scholar 

  28. M. Delakis, C. Garcia, Text detection with convolutional neural networks, in Proceedings of VISAPP (2008)

    Google Scholar 

  29. H. Xu, F. Su, Robust seed localization and growing with deep convolutional features for scene text detection, in Proceedings of ICMR (2015)

    Google Scholar 

  30. M. Jaderberg, K. Simonyan, A. Vedaldi, A. Zisserman, Deep structured output learning for unconstrained text recognition, in Proceedings of ICLR (2015)

    Google Scholar 

  31. A. Karpathy, G. Toderici, S. Shetty, T. Leung, R. Sukthankar, L. Fei-Fei, Large-scale video classification with convolutional, in Proceedings of CVPR (2014)

    Google Scholar 

  32. W. Yin, K. Kann, M. Yu, H. Schutze, Comparative study of CNN and RNN for natural language processing (2017). arXiv:1702.01923 [cs.CL]

  33. L. Li, L. Jin, D. Huang, Exploring recurrent neural networks to detect named entities from biomedical text, in Chinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big Data, ed. by M. Sun, Z. Liu, M. Zhang, Y. Liu, Lecture Notes in Computer Science, vol. 9427 (Springer, Cham, 2015)

    Google Scholar 

  34. D. Lopez-Paz, K. Muandet, B. Schölkopf, I. Tolstikhin, Towards a learning theory of cause-effect inference, in International Conference on Machine Learning, pp. 1452–1461 (2015)

    Google Scholar 

  35. S. Min, B. Lee, S. Yoon, Deep learning in bioinformatics. Brief. Bioinform. (2016)

    Google Scholar 

  36. V. Trevino, F. Falciani, H.A. Barrera-Saldaña, DNA microarrays: a powerful genomic tool for biomedical and clinical research. Mol. Med. 13(9–10), 527–541 (2007)

    Google Scholar 

  37. D.R. Edla, P.K. Jana, S. Machavarapu, KD-tree based clustering for gene expression data, Encyclopedia of Business Analytics and Optimization, p. 15 (2014)

    Google Scholar 

  38. R. Fakoor, F. Ladhak, A. Nazi, M. Huber, Using deep learning to enhance cancer diagnosis and classification, in Proceedings of the 30th International Conference on Machine Learning, Atlanta, USA (2013)

    Google Scholar 

  39. Y. Lu, J. Han, Cancer classification using gene expression data. Inf. Syst. Data Manag. Bioinform. 28(4), 243–268 (2003). Elsevier

    MATH  Google Scholar 

  40. A. Brazma, J. Vilo, Gene expression data analysis. FEBS Lett. 480(1), 17–24 (2000)

    Article  Google Scholar 

  41. J. Clemente, gene expression data analysis, LinkedIn, 28 Jan 2013, http://www.slideshare.net/JhoireneClemente/gene-expression-data-analysis. Accessed 8 Jan 2017

  42. J. Liu, W. Cai, X. Shao, Cancer classification based on microarray gene expression data using a principal component accumulation method. Sci. China Chem. 54(5), 802–811 (2011)

    Article  Google Scholar 

  43. H. Zhang, C.-Y. Yu, B. Singer, Cell and tumor classification using gene expression data: construction of forests. PNAS 100(7), 4168–4172 (2003)

    Article  Google Scholar 

  44. O. Dagliyan, F. Uney-Yuksektepe, I.H. Kavakli, M. Turkay, Optimization based tumor classification from microarray gene expression data. PLOS 6(2) (2011)

    Article  Google Scholar 

  45. T.C. Siang, W.T. Soon, S. Kasim, M.S. Mohamad, C.W. Howe, S. Deris, Z. Zakaria, Z.A. Shah, Z. Ibrahim, A review of cancer classification software for gene expression data. Int. J. Bio-Sci. Bio-Technol. 7(4), 89–108 (2015)

    Article  Google Scholar 

  46. A. Gupta, H. Wang, M. Ganapathiraju, Learning structure in gene expression data using deep architectures, with an application to gene clustering, in IEEE Workshop on Biomedical Visual Search and Deep Learning, Washington D.C (2015)

    Google Scholar 

  47. Y. Chen, R. Narayan, A. Subramanian, X. Xie, Gene expression inference with deep learning. Bioinformatics (2015)

    Google Scholar 

  48. J. Tan, J.H. Hammond, D.A. Hogan, C.S. Greene, ADAGE-based integration of publicly available pseudomonas aeruginosa gene expression data with denoising autoencoders illuminates microbe-host interactions. mSystems 1(1) (2016)

    Google Scholar 

  49. C. Angermueller, T. Parnamaa, L. Parts, O. Stegle, Deep learning for computational biology. Mol. Syst. Biol. 12(878) (2016)

    Article  Google Scholar 

  50. J. Tan, M. Ung, C. Cheng, C.S. Greene, Unsupervised feature construction and knowledge extraction from genome-wide assays of breast cancer with denoising autoencoders, in Pacific Symposium on Biocomputing (2015)

    Google Scholar 

  51. Q.V. Lee, J. Han, J.W. Gray, P.T. Spellman, A. Borowsky, B. Parvin, Learning invariant features of tumor signatures, in Proceedings of ISBI, pp. 302–305 (2012)

    Google Scholar 

  52. B. Alipanahi, A. Delong, M. Weirauch, B.J. Frey, DeepBind: predicting the sequence specificities of DNA-and RNA-binding proteins by deep learning. Nat. Biotechnol. (2015)

    Google Scholar 

  53. H. Zeng, M. Edwards, G. Liu, D.K. Gifford, Convolutional neural network architectures for predicting DNA–protein binding. Bioinformatics 32(12) (2016)

    Article  Google Scholar 

  54. D. Kelly, J. Snoek, J.B. Rinn, Learning the regulatory code of the accessible genome with deep convolutional neural networks. bioRxiv (2015)

    Google Scholar 

  55. D. Pokholok, C. Harbison, S. Levine, M. Cole, N. Hannet, T. Lee, G. Bell, K. Walker, P. Rolfe, E. Herbolsheimer, J. Zeitlinger, F. Lewitter, D. Gifford, R. Young, Genome-wide map of nucleosome. Cell 122, 517–527 (2005)

    Article  Google Scholar 

  56. R. Singh, J. Lanchantin, G. Robins, Y. Qi, DeepChrome: deep learning for predicting gene expression from histone modfications. Bioinformatics 32, i639–i648 (2016)

    Article  Google Scholar 

  57. R. Xu, D.C. Wunsch II, R.L. Frank, Inference of genetic regulatory networks with recurrent neural network models using particle swarm optimisation. IEEE Trans. Comput. Biol. Bioinform. (2006)

    Google Scholar 

  58. N. Noman, L. Palafox, H. Iba, Reconstruction of gene regulatory networks from gene expression data using decoupled recurrent neural network model, in Proceeding in Information and Communication Technology (PICT 6), pp. 93–103 (2013)

    Google Scholar 

  59. B. Lee, J. Baek, S. Park, S. Yoon, DeepTarget: end-to-end learning framework for microRNA target prediction using deep recurrent neural networks, in Proceedings of BCB ’16. ACM (2016)

    Google Scholar 

  60. A. Khan, S. Mandal, R.K. Pal, G. Saha, Construction of gene regulatory networks using recurrent neural networks and swarm intelligence. Scientifica (2016)

    Google Scholar 

  61. P. Danaee, R. Ghaeini, D.A. Hendrix, A deep learning method for cancer detection and relevant gene identification, in Pacific Symposium on Biocomputing, vol. 22, pp. 219–229 (2016)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. University of Canberra, Canberra, Australia

    Dinesh Kumar & Dharmendra Sharma

Authors
  1. Dinesh Kumar
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Dharmendra Sharma
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Dinesh Kumar .

Editor information

Editors and Affiliations

  1. Aurel Vlaicu University of Arad, Arad, Romania

    Valentina Emilia Balas

  2. School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India

    Sanjiban Sekhar Roy

  3. University of Canberra, Bruce, ACT, Australia

    Dharmendra Sharma

  4. Department of Civil Engineering, National Institute of Technology Patna, Patna, Bihar, India

    Pijush Samui

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Kumar, D., Sharma, D. (2019). Deep Learning in Gene Expression Modeling. In: Balas, V., Roy, S., Sharma, D., Samui, P. (eds) Handbook of Deep Learning Applications. Smart Innovation, Systems and Technologies, vol 136. Springer, Cham. https://doi.org/10.1007/978-3-030-11479-4_17

Download citation

Publish with us

Policies and ethics

Search

Navigation