New Gene Selection Method Using Gene Expression Programing Approach on Microarray Data Sets

2018 ◽  
pp. 17-31 ◽  
Author(s):  
Russul Alanni ◽  
Jingyu Hou ◽  
Hasseeb Azzawi ◽  
Yong Xiang
Keyword(s):  
Gene Expression ◽  
Microarray Data ◽  
Gene Selection ◽  
Selection Method ◽  
Data Sets ◽  
Gene Selection Method ◽  
New Gene

A novel aggregate gene selection method for microarray data classification

2015 ◽  
Vol 60-61 ◽  
pp. 16-23 ◽  
Author(s):  
Thanh Nguyen ◽  
Abbas Khosravi ◽  
Douglas Creighton ◽  
Saeid Nahavandi
Keyword(s):  
Microarray Data ◽  
Gene Selection ◽  
Data Classification ◽  
Selection Method ◽  
Gene Selection Method

A centroid-based gene selection method for microarray data classification

2016 ◽  
Vol 400 ◽  
pp. 32-41 ◽  
Author(s):  
Shun Guo ◽  
Donghui Guo ◽  
Lifei Chen ◽  
Qingshan Jiang
Keyword(s):  
Microarray Data ◽  
Gene Selection ◽  
Data Classification ◽  
Selection Method ◽  
Gene Selection Method

scholarly journals An embedded gene selection method using knockoffs optimizing neural network

2020 ◽  
Vol 21 (1) ◽  
Author(s):  
Juncheng Guo ◽  
Min Jin ◽  
Yuanyuan Chen ◽  
Jianxiao Liu
Keyword(s):  
Neural Network ◽  
Gene Expression ◽  
Gene Selection ◽  
Expression Profiles ◽  
Research Work ◽  
Selection Method ◽  
Small Subset ◽  
Phenotypic Traits ◽  
Breast Cancer Dataset ◽  
Gene Selection Method

Abstract Background Gene selection refers to find a small subset of discriminant genes from the gene expression profiles. How to select genes that affect specific phenotypic traits effectively is an important research work in the field of biology. The neural network has better fitting ability when dealing with nonlinear data, and it can capture features automatically and flexibly. In this work, we propose an embedded gene selection method using neural network. The important genes can be obtained by calculating the weight coefficient after the training is completed. In order to solve the problem of black box of neural network and further make the training results interpretable in neural network, we use the idea of knockoffs to construct the knockoff feature genes of the original feature genes. This method not only make each feature gene to compete with each other, but also make each feature gene compete with its knockoff feature gene. This approach can help to select the key genes that affect the decision-making of neural networks. Results We use maize carotenoids, tocopherol methyltransferase, raffinose family oligosaccharides and human breast cancer dataset to do verification and analysis. Conclusions The experiment results demonstrate that the knockoffs optimizing neural network method has better detection effect than the other existing algorithms, and specially for processing the nonlinear gene expression and phenotype data.

Sign in / Sign up

Export Citation Format

Share Document