A review on convolutional neural network based deep learning methods in gene expression data for disease diagnosis

被引:11
|
作者
Gunavathi, C. [1 ]
Sivasubramanian, K. [2 ]
Keerthika, P. [3 ]
Paramasivam, C. [4 ]
机构
[1] Vellore Inst Technol, Sch Informat Technol & Engn, Vellore 632014, Tamil Nadu, India
[2] KS Rangasamy Coll Technol, Dept Elect & Commun Engn, Tiruchengode 637215, Tamil Nadu, India
[3] Kongu Engn Coll, Dept Comp Sci & Engn, Erode 638060, Tamil Nadu, India
[4] Amrita Vishwa Vidyapeetham, Dept Elect & Commun Engn, Bengaluru 560035, Karnataka, India
关键词
Gene expression; Deep learning; Convolutional neural network; Classification; Microarray; RNA-Seq; CLASSIFICATION;
D O I
10.1016/j.matpr.2020.10.263
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
Bioinformatics is the discipline of employing informatics technologies on biological datasets to extract the hidden knowledge from both biology and computer science fields. Gene expression datasets are widely used in disease prediction and diagnosis especially in cancer treatment. There are many computational techniques that are available for gene expression analysis. Deep learning methods are a fragment of machine learning techniques which are based on artificial neural networks. Convolutional neural networks are the most important deep learning model that is designed for data that comes in the form of multidimensional arrays. This paper reviews the recent research works that utilize convolutional neural network deep learning methods on gene expression data analysis. (c) 2021 Elsevier Ltd. All rights reserved. Selection and peer-review under responsibility of the scientific committee of the International Conference on Advances in Materials Research-2019.
引用
收藏
页码:2282 / 2285
页数:4
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