GSEnet: feature extraction of gene expression data and its application to Leukemia classification

GSEnet: feature extraction of gene expression data and its application to Leukemia classification

Gene expression data is highly dimensional. As disease-related genes account for only a tiny fraction, a deep learning model, namely GSEnet, is proposed to extract instructive features from gene expression data. This model consists of three modules, namely the pre-conv module, the SE-Resnet module,...

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Bibliographic Details
Main Authors: Kun Yu, Mingxu Huang, Shuaizheng Chen, Chaolu Feng, Wei Li
Format: Article
Language:English
Published: AIMS Press 2022-03-01
Series:Mathematical Biosciences and Engineering
Subjects:
deep learning
gene expression
feature extraction
resnet
senet
Online Access:https://www.aimspress.com/article/doi/10.3934/mbe.2022228?viewType=HTML

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https://www.aimspress.com/article/doi/10.3934/mbe.2022228?viewType=HTML

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