Abstract
Gene expression sample data, which usually contains massive expression profiles of genes, is commonly used for disease related gene analysis. The selection of relevant genes from huge amount of genes is always a fundamental process in applications of gene expression data. As more and more genes have been detected, the size of gene expression data becomes larger and larger; this challenges the computing efficiency for extracting the relevant and important genes from gene expression data. In this paper, we provide a novel Bi-dimensional Principal Feature Selection (BPFS) method for efficiently extracting critical genes from big gene expression data. It applies the principal component analysis (PCA) method on sample and gene domains successively, aiming at extracting the relevant gene features and reducing redundancies while losing less information. The experimental results on four real-world cancer gene expression datasets show that the proposed BPFS method greatly reduces the data size and achieves a nearly double processing speed compared to the counterpart methods, while maintaining better accuracy and effectiveness.
Funder
Australian Research Council
Publisher
Public Library of Science (PLoS)
Reference36 articles.
1. Differential gene expression data from the human central nervous system across alzheimer’s disease, lewy body diseases, and the amyotrophic lateral sclerosis and frontotemporal dementia spectrum;Ayush Noori;Data in Brief,2021
2. Machine learning and bioinformatics models to identify gene expression patterns of ovarian cancer associated with disease progression and mortality;Md Ali Hossain;Journal of biomedical informatics,2019
3. Gene co-expression analysis for functional classification and gene–disease predictions;Dam Sipko Van;Briefings in bioinformatics,2018
4. Inference of differential gene regulatory networks based on gene expression and genetic perturbation data;Xin Zhou;Bioinformatics,2019
5. A graph-embedded deep feedforward network for disease outcome classification and feature selection using gene expression data;Yunchuan Kong;Bioinformatics,2018
Cited by
2 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献