A graph-embedded deep feedforward network for disease outcome classification and feature selection using gene expression data

Gene expression data represents a unique challenge in predictive model building, because of the small number of samples \((n)\) compared to the huge amount of features \((p)\). This "\(n

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Bibliographic Details
Published in:arXiv.org 2018-02
Main Authors: Kong, Yunchuan, Yu, Tianwei
Format: Article
Language:English
Subjects:
Artificial neural networks
Cancer
Classification
Computer simulation
Data analysis
Gene expression
Machine learning
Neural networks
Ribonucleic acid
RNA
Online Access:Get full text
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Summary:Gene expression data represents a unique challenge in predictive model building, because of the small number of samples \((n)\) compared to the huge amount of features \((p)\). This "\(n
ISSN:2331-8422