NDLI: An embedded gene selection method using knockoffs optimizing neural network

Content Provider	Springer Nature : BioMed Central
Author	Guo, Juncheng Jin, Min Chen, Yuanyuan Liu, Jianxiao
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.
Related Links	https://bmcbioinformatics.biomedcentral.com/counter/pdf/10.1186/s12859-020-03717-w.pdf
Ending Page	19
Page Count	19
Starting Page	1
File Format	HTM / HTML
ISSN	14712105
DOI	10.1186/s12859-020-03717-w
Journal	BMC Bioinformatics
Issue Number	1
Volume Number	21
Language	English
Publisher	BioMed Central
Publisher Date	2020-09-22
Access Restriction	Open
Subject Keyword	Bioinformatics Microarrays Computational Biology Computer Appl. in Life Sciences Algorithms Gene mining Neural network Knockoffs Nonlinear data Maize Computational Biology/Bioinformatics
Content Type	Text
Resource Type	Article
Subject	Molecular Biology Biochemistry Computer Science Applications Applied Mathematics Structural Biology
Journal Impact Factor	2.9/2023
5-Year Journal Impact Factor	3.6/2023

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

A novel biomarker selection method combining graph neural network and gene relationships applied to microarray data

A combinational feature selection and ensemble neural network method for classification of gene expression data

Nonlinear Data Mining of Microarray Data Using Michaelis Menten Functions

Additional Neural Matrix Factorization model for computational drug repositioning

Predicting functions of maize proteins using graph convolutional network

Feature selection of gene expression data for Cancer classification using double RBF-kernels

Inferring gene regulatory networks from classified microarray data: Initial results

Robust gene selection methods using weighting schemes for microarray data analysis

FiGS: a filter-based gene selection workbench for microarray data

An embedded gene selection method using knockoffs optimizing neural network

Similar Documents

A novel biomarker selection method combining graph neural network and gene relationships applied to microarray data

A combinational feature selection and ensemble neural network method for classification of gene expression data

Nonlinear Data Mining of Microarray Data Using Michaelis Menten Functions

Additional Neural Matrix Factorization model for computational drug repositioning

Predicting functions of maize proteins using graph convolutional network

Feature selection of gene expression data for Cancer classification using double RBF-kernels

Inferring gene regulatory networks from classified microarray data: Initial results

Robust gene selection methods using weighting schemes for microarray data analysis

FiGS: a filter-based gene selection workbench for microarray data

An embedded gene selection method using knockoffs optimizing neural network