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1-Dimensional Convolution Neural Network Classification Technique for Gene Expression Data

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Deep Learning for Biomedical Data Analysis

Abstract

In the field of bioinformatics, the development of computational methods has drawn significant interest in predicting clinical outcomes of biological data, which has a large number of features. DNA microarray technology is an approach to monitor the expression levels of sizable genes simultaneously. Microarray gene expression data is more useful for predicting and understanding various diseases such as cancer. Most of the microarray data are believed to be high dimensional, redundant, and noisy. In recent years, deep learning has become a research topic in the field of Machine Learning (ML) that achieves remarkable results in learning high-level latent features within identical samples. This chapter discusses various filter techniques which reduce the high dimensionality of microarray data and different deep learning classification techniques such as 2-Dimensional Convolution Neural Network (2D- CNN) and 1-Dimensional CNN (1D-CNN). The proposed method used the fisher criterion and 1D-CNN techniques for microarray cancer samples prediction.

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  1. 1.

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Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, Indian Institute of Technology, Dhanbad, Jharkhand, India

    Samson Anosh Babu Parisapogu & Chandra Sekhara Rao Annavarapu

  2. Faculty of Computing and Information Technology, The University of Bisha, Bisha, Saudi Arabia

    Mourad Elloumi

Authors
  1. Samson Anosh Babu Parisapogu
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  2. Chandra Sekhara Rao Annavarapu
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  3. Mourad Elloumi
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Corresponding author

Correspondence to Chandra Sekhara Rao Annavarapu .

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Editors and Affiliations

  1. Computing and Information Technology, The University of Bisha, Bisha, Saudi Arabia

    Mourad Elloumi

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Parisapogu, S.A.B., Annavarapu, C.S.R., Elloumi, M. (2021). 1-Dimensional Convolution Neural Network Classification Technique for Gene Expression Data. In: Elloumi, M. (eds) Deep Learning for Biomedical Data Analysis. Springer, Cham. https://doi.org/10.1007/978-3-030-71676-9_1

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