Gene Selection Using Locality Sensitive Laplacian Score

Gene selection based on microarray data, is highly important for classifying tumors accurately. Existing gene selection schemes are mainly based on ranking statistics. From manifold learning standpoint, local geometrical structure is more essential to characterize features compared with global infor...

Full description

Saved in:
Bibliographic Details
Published in:IEEE/ACM transactions on computational biology and bioinformatics 2014-11, Vol.11 (6), p.1146-1156
Main Authors: Liao, Bo, Jiang, Yan, Liang, Wei, Zhu, Wen, Cai, Lijun, Cao, Zhi
Format: Article
Language:English
Subjects:
Algorithms
Artificial Intelligence
Bioinformatics
Biology
Biomarkers
Classification
Computational biology
Computational Biology - methods
Feature extraction
Gene expression
Gene Expression Profiling - methods
Genes
Genomics
Humans
Laplace equations
Learning
Models, Statistical
Neoplasms - genetics
Neoplasms - metabolism
Statistics
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Gene selection based on microarray data, is highly important for classifying tumors accurately. Existing gene selection schemes are mainly based on ranking statistics. From manifold learning standpoint, local geometrical structure is more essential to characterize features compared with global information. In this study, we propose a supervised gene selection method called locality sensitive Laplacian score (LSLS), which incorporates discriminative information into local geometrical structure, by minimizing local within-class information and maximizing local between-class information simultaneously. In addition, variance information is considered in our algorithm framework. Eventually, to find more superior gene subsets, which is significant for biomarker discovery, a two-stage feature selection method that combines the LSLS and wrapper method (sequential forward selection or sequential backward selection) is presented. Experimental results of six publicly available gene expression profile data sets demonstrate the effectiveness of the proposed approach compared with a number of state-of-the-art gene selection methods.
ISSN:1545-5963
1557-9964
DOI:10.1109/TCBB.2014.2328334