Feature genes selection using supervised locally linear embedding and correlation coefficient for microarray classification

DOI10.1155/2018/5490513zbMath1409.92091OpenAlexW2787022463WikidataQ52318583 ScholiaQ52318583MaRDI QIDQ1731297

Jiucheng Xu, Huiyu Mu, Fangzhou Huang, Yun Wang

Publication date: 13 March 2019

Published in: Computational \& Mathematical Methods in Medicine (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1155/2018/5490513

zbMATH Keywords

Spearman's rank correlation coefficientfeature genes selectiontumor microarray classification

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10) Biochemistry, molecular biology (92C40) Software, source code, etc. for problems pertaining to biology (92-04)

Related Items (2)

Feature genes selection using fuzzy rough uncertainty metric for tumor diagnosis ⋮ Feature selection using neighborhood entropy-based uncertainty measures for gene expression data classification

Uses Software

• SparseLOGREG

Cites Work

• Unnamed Item
• Cancer feature selection and classification using a binary quantum-behaved particle swarm optimization and support vector machine
• Supervised locally linear embedding with probability-based distance for classification
• Incremental locally linear embedding-based fault detection for satellite attitude control systems
• Probing for sparse and fast variable selection with model-based boosting
• Gene selection for cancer classification using support vector machines

This page was built for publication: Feature genes selection using supervised locally linear embedding and correlation coefficient for microarray classification