Gene Correlation Guided Gene Selection for Microarray Data Classification

The microarray cancer data obtained by DNA microarray technology play an important role for cancer prevention, diagnosis, and treatment. However, predicting the different types of tumors is a challenging task since the sample size in microarray data is often small but the dimensionality is very high...

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Bibliographic Details
Published in:BioMed research international 2021, Vol.2021 (1), p.6490118-6490118
Main Authors: Yang, Dong, Zhu, Xuchang
Format: Article
Language:English
Subjects:
Algorithms
Cancer
Cell division
Classification
Correlation
Data mining
Deoxyribonucleic acid
Dictionaries
DNA
DNA chips
DNA microarrays
Feature selection
Genes
Genetic algorithms
Genetic aspects
Graphs
Iterative methods
Machine learning
Methods
Oncology, Experimental
Optimization
Sparsity
Tumors
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Summary:The microarray cancer data obtained by DNA microarray technology play an important role for cancer prevention, diagnosis, and treatment. However, predicting the different types of tumors is a challenging task since the sample size in microarray data is often small but the dimensionality is very high. Gene selection, which is an effective means, is aimed at mitigating the curse of dimensionality problem and can boost the classification accuracy of microarray data. However, many of previous gene selection methods focus on model design, but neglect the correlation between different genes. In this paper, we introduce a novel unsupervised gene selection method by taking the gene correlation into consideration, named gene correlation guided gene selection (G3CS). Specifically, we calculate the covariance of different gene dimension pairs and embed it into our unsupervised gene selection model to regularize the gene selection coefficient matrix. In such a manner, redundant genes can be effectively excluded. In addition, we utilize a matrix factorization term to exploit the cluster structure of original microarray data to assist the learning process. We design an iterative updating algorithm with convergence guarantee to solve the resultant optimization problem. Experimental results on six publicly available microarray datasets are conducted to validate the efficacy of our proposed method.
ISSN:2314-6133
2314-6141
DOI:10.1155/2021/6490118