Paper Info
Title
An efficient search algorithm for biomarker selection from RNA-seq prostate cancer data.
Abstract
RNA-sequencing technology helps to consider the expression of thousands of genes, simultaneously. The largescale gene expression data include a huge number of genes versus a few samples. Therefore, the algorithms that among huge number of unrelated genes can accurately detect genes associated with specific disease can be useful for experts in early detect and treat the disease. A two-phase search algorithm is proposed in this paper to discover the biomarkers in the RNA-seq gene expression dataset for the prostate cancer diagnosis. After statistical noise removing from the original large-scale dataset, a multi-objective optimization process is proposed to select the best non-dominated subset of genes with the maximum classification accuracy and the minimum number of genes, simultaneously. Finally, the proposed cache-based modification of the sequential forward floating selection (CMSFFS) algorithm is applied to the selected subset of genes to discover the most discriminant genes. The obtained results show that the proposed algorithm is able to achieve the classification accuracy, sensitivity and specificity of 100% in the large scale RNA-seq prostate cancer dataset by selecting only three biomarkers.
Year
DOI
Venue
2018
10.3233/JIFS-171297
JOURNAL OF INTELLIGENT & FUZZY SYSTEMS
Keywords
Field
DocType
RNA-seq,large-scale prostate cancer data,two-phase search algorithm,multi-objective-based optimization,CMSFFS
Search algorithm,RNA-Seq,Biomarker (medicine),Prostate cancer,Artificial intelligence,Machine learning,Mathematics
Journal
Volume
Issue
ISSN
35
3
1064-1246
Citations 
PageRank 
References 
0
0.34
9
Authors
4
Name
Order
Citations
PageRank
S. Shahbeig1121.23
A. Rahideh2293.92
Mohammad Sadegh Helfroush37011.30
Kamran Kazemi48112.24