Selection of interdependent genes via dynamic relevance analysis for cancer diagnosis

Authors:
Xin Sun;Yanheng Liu;Da Wei;Mantao Xu;Huiling Chen;Jiawei Han
Affiliations:
College of Computer Science and Technology, Jilin University, Changchun, Jilin 130012, China and School of Computing, University of Eastern Finland, Joensuu FIN-80101, Finland;College of Computer Science and Technology, Jilin University, Changchun, Jilin 130012, China;College of Computer Science and Technology, Jilin University, Changchun, Jilin 130012, China;School of Computing, University of Eastern Finland, Joensuu FIN-80101, Finland and School of Electrical Engineering, Shanghai Dianji University, Shanghai 200240, China;School of Physics and Electronic Information, Wenzhou University, Wenzhou 325035, China;College of Computer Science and Technology, Jilin University, Changchun, Jilin 130012, China
Venue:
Journal of Biomedical Informatics
Year:
2013

Citing 22
Cited 0

Minimum Redundancy Feature Selection from Microarray Gene Expression Data

CSB '03 Proceedings of the IEEE Computer Society Conference on Bioinformatics
Theoretical and Empirical Analysis of ReliefF and RReliefF

Machine Learning
An introduction to variable and feature selection

The Journal of Machine Learning Research
Using Uncorrelated Discriminant Analysis for Tissue Classification with Gene Expression Data

IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Efficient Feature Selection via Analysis of Relevance and Redundancy

The Journal of Machine Learning Research
Feature Selection Based on Mutual Information: Criteria of Max-Dependency, Max-Relevance, and Min-Redundancy

IEEE Transactions on Pattern Analysis and Machine Intelligence
A comparative study of feature selection and multiclass classification methods for tissue classification based on gene expression

Bioinformatics
A comprehensive evaluation of multicategory classification methods for microarray gene expression cancer diagnosis

Bioinformatics
An effective structure learning method for constructing gene networks

Bioinformatics
Statistical Comparisons of Classifiers over Multiple Data Sets

The Journal of Machine Learning Research
A review of feature selection techniques in bioinformatics

Bioinformatics
Combining partial correlation and an information theory approach to the reversed engineering of gene co-expression networks

Bioinformatics
New gene selection method for multiclass tumor classification by class centroid

Journal of Biomedical Informatics
Information gain and divergence-based feature selection for machine learning-based text categorization

Information Processing and Management: an International Journal - Special issue: Formal methods for information retrieval
A novel feature selection approach for biomedical data classification

Journal of Biomedical Informatics
Ensemble gene selection by grouping for microarray data classification

Journal of Biomedical Informatics
Feature Selection for Gene Expression Using Model-Based Entropy

IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Selecting few genes for microarray gene expression classification

CAEPIA'09 Proceedings of the Current topics in artificial intelligence, and 13th conference on Spanish association for artificial intelligence
An efficient statistical feature selection approach for classification of gene expression data

Journal of Biomedical Informatics
An empirical approach to model selection through validation for censored survival data

Journal of Biomedical Informatics
Feature evaluation and selection with cooperative game theory

Pattern Recognition
Optimal Search-Based Gene Subset Selection for Gene Array Cancer Classification

IEEE Transactions on Information Technology in Biomedicine

Quantified Score

Hi-index	0.00

Visualization

Abstract

Microarray analysis is widely accepted for human cancer diagnosis and classification. However the high dimensionality of microarray data poses a great challenge to classification. Gene selection plays a key role in identifying salient genes from thousands of genes in microarray data that can directly contribute to the symptom of disease. Although various excellent selection methods are currently available, one common problem of these methods is that genes which have strong discriminatory power as a group but are weak as individuals will be discarded. In this paper, a new gene selection method is proposed for cancer diagnosis and classification by retaining useful intrinsic groups of interdependent genes. The primary characteristic of this method is that the relevance between each gene and target will be dynamically updated when a new gene is selected. The effectiveness of our method is validated by experiments on six publicly available microarray data sets. Experimental results show that the classification performance and enrichment score achieved by our proposed method is better than those of other selection methods.