A Review of Matched-pairs Feature Selection Methods for Gene Expression Data Analysis-Reference-Cited by-同舟云学术

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A Review of Matched-pairs Feature Selection Methods for Gene Expression Data Analysis

Published:2018 Issue: Volume:16 Page:88-97
ISSN:2001-0370
Container-title:Computational and Structural Biotechnology Journal
language:en
Short-container-title:Computational and Structural Biotechnology Journal

Author:

Liang Sen^ORCID,Ma Anjun,Yang Sen,Wang Yan,Ma Qin^ORCID

Funder

National Natural Science Foundation of China

International Cooperation and Exchanges NSFC

Development Project of Jilin Province of China

National Science Foundation/EPSCoR

State of South Dakota Research Innovation Center

Agriculture Experiment Station of South Dakota State University

Sanford Health – South Dakota State University Collaborative Research Seed Grant Program

National Science Foundation

Publisher

Elsevier BV

Subject

Computer Science Applications,Genetics,Biochemistry,Structural Biology,Biophysics,Biotechnology

Reference93 articles.

1. Machine learning applications in cancer prognosis and prediction;Kourou;Comput Struct Biotechnol J,2015

2. New feature selection method based on neural network and machine learning;Challita,2016

3. A survey on semi-supervised feature selection methods;Sheikhpour;Pattern Recog,2017

4. A survey on feature selection methods;Chandrashekar;Comput Electr Eng,2014

5. Sparse representation and learning in visual recognition: theory and applications;Cheng;Signal Process,2013

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