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A Study of Gene Characteristics and Their Applications Using Deep Learning

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Handbook of Machine Learning Applications for Genomics

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

Abstract

DNA sequencing deals with figuring out the order of arrangement of the bases in the DNA. These bases are the building blocks of DNA molecules and their arrangement mostly determines the genetic information carried within a DNA segment, therefore sequencing becomes a very important aspect in the field of genomics. Now it becomes ever more important to optimize this process of sequencing and analysis and the field of deep learning has a lot to offer. Autoencoders are artificial neural networks which are trained in an unsupervised manner to obtain feature representation or dimensionality reduction. Now as clustering is difficult to perform for data with large dimensions, autoencoders can be used to reduce the dimension of data by associating each gene cluster with an autoencoder. Genetic algorithms are algorithms which are based on Darwin’s law of evolution and provide a better alternative to traditional clustering algorithms which have been found to have various drawbacks when implemented for genetic data. Drug repositioning is the examination of existing drugs on new disease targets and pharmacogenomics, looking to predict the target’s response to a drug. Deep learning acts as a powerful tool for repositioning drugs by allowing us to perform robust predictions and provide deep insights to drug-disease combinations. This chapter aims to provide the reader with various deep learning models and analysis algorithms which have been employed in some or the other forms for studying gene characteristics and gene development or have the potential to form the basis for ground breaking research for the same.

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Authors and Affiliations

  1. School of Computer Science and Engineering, VIT, Vellore, Tamil Nadu, 632014, India

    Prajjwal Gupta & Saransh Bhachawat

  2. School of Information Technology and Engineering, VIT, Vellore, 632014, India

    Kshitij Dhyani & B.K. Tripathy

Authors
  1. Prajjwal Gupta
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  2. Saransh Bhachawat
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  3. Kshitij Dhyani
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  4. 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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Gupta, P., Bhachawat, S., Dhyani, K., Tripathy, B. (2022). A Study of Gene Characteristics and Their Applications Using Deep Learning. 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_4

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