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Computational Biology in the Lens of CNN

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Handbook of Machine Learning Applications for Genomics

Part of the book series: Studies in Big Data ((SBD,volume 103))

Abstract

Throughout this chapter the objective is to bring deep learning techniques and algorithms, specifically CNN, which bring about ease for a researcher with respect to time and resources. The concepts are explained as an overview to implant an intuition of the techniques which can be further elaborated with the mathematics in the references. Computational biology involves the examination of how proteins interact with each other through the simulation of protein folding, motion, and interaction. Current computational biology research can be divided into a number of broad areas, mainly based on the type of experimental data that is analyzed or modeled. Deep learning and in particular, Convolutional Neural Networks (CNNs) has brought about a revolution for the analysis of gene expression images. This technique solves some of the setbacks faced by traditional machine learning approaches while advances in technology have enabled the capture of gene sequence images, while in some cases non-image data captured can be converted to an image for analysis.

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Author information

Authors and Affiliations

  1. School of Computer Science and Engineering, Vellore Institute of Technology (VIT), Vellore, Tamil Nadu, 632014, India

    Pranjal Bhardwaj

  2. School of Information Technology and Engineering, Vellore Institute of Technology (VIT), Vellore, Tamil Nadu, 632014, India

    Thejineaswar Guhan & B.K. Tripathy

Authors
  1. Pranjal Bhardwaj
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  2. Thejineaswar Guhan
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  3. B.K. Tripathy
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Corresponding author

Correspondence to B.K. Tripathy .

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Editors and Affiliations

  1. Vellore Institute of Technology Universi, Vellore, India

    Sanjiban Sekhar Roy

  2. Department of Physics, Chuo University, Tokyo, Tokyo, Japan

    Y.-H. Taguchi

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Bhardwaj, P., Guhan, T., Tripathy, B. (2022). Computational Biology in the Lens of CNN. In: Roy, S.S., Taguchi, YH. (eds) Handbook of Machine Learning Applications for Genomics. Studies in Big Data, vol 103. Springer, Singapore. https://doi.org/10.1007/978-981-16-9158-4_5

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