scholarly journals An ensemble correlation-based gene selection algorithm for cancer classification with gene expression data

2012 ◽  
Vol 28 (24) ◽  
pp. 3306-3315 ◽  
Author(s):  
Y. Piao ◽  
M. Piao ◽  
K. Park ◽  
K. H. Ryu
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Gene Selection ◽  
Cancer Classification ◽  
Expression Data ◽  
Selection Algorithm ◽  
Gene Selection Algorithm ◽  
Ensemble Correlation

scholarly journals A robust and stable gene selection algorithm based on graph theory and machine learning

2021 ◽  
Vol 15 (1) ◽  
Author(s):  
Subrata Saha ◽  
Ahmed Soliman ◽  
Sanguthevar Rajasekaran
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Gene Selection ◽  
Small Sample ◽  
Stable Gene ◽  
Large Set ◽  
Expression Data ◽  
Selection Algorithm ◽  
Prediction Ability ◽  
Gene Selection Algorithm

Abstract Background Nowadays we are observing an explosion of gene expression data with phenotypes. It enables us to accurately identify genes responsible for certain medical condition as well as classify them for drug target. Like any other phenotype data in medical domain, gene expression data with phenotypes also suffer from being a very underdetermined system. In a very large set of features but a very small sample size domain (e.g. DNA microarray, RNA-seq data, GWAS data, etc.), it is often reported that several contrasting feature subsets may yield near equally optimal results. This phenomenon is known as instability. Considering these facts, we have developed a robust and stable supervised gene selection algorithm to select a set of robust and stable genes having a better prediction ability from the gene expression datasets with phenotypes. Stability and robustness is ensured by class and instance level perturbations, respectively. Results We have performed rigorous experimental evaluations using 10 real gene expression microarray datasets with phenotypes. They reveal that our algorithm outperforms the state-of-the-art algorithms with respect to stability and classification accuracy. We have also performed biological enrichment analysis based on gene ontology-biological processes (GO-BP) terms, disease ontology (DO) terms, and biological pathways. Conclusions It is indisputable from the results of the performance evaluations that our proposed method is indeed an effective and efficient supervised gene selection algorithm.

RSGSA: a Robust and Stable Gene Selection Algorithm

2020 ◽  
Author(s):  
Subrata Saha ◽  
Ahmed Soliman ◽  
Sanguthevar Rajasekaran
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Gene Selection ◽  
Medical Condition ◽  
Small Sample ◽  
Stable Gene ◽  
Large Set ◽  
Expression Data ◽  
Selection Algorithm ◽  
Gene Selection Algorithm

AbstractNowadays we are observing an explosion of gene expression data with phenotypes. It enables researchers to efficiently identify genes responsible for certain medical condition as well as classify them for drug target. Like any other phenotype data in medical domain, gene expression data with phenotypes also suffers from being very underdetermined system. In a very large set of features but a very small sample size domains (e.g., DNA microarray, RNA-seq data, GWAS data, etc.), it is often reported that several different spurious feature subsets may yield equally optimal results. This phenomenon is known as instability. Considering these facts, we have developed a very robust and stable supervised gene selection algorithm to select the most discriminating non-spurious set of genes from the gene expression datasets with phenotypes. Stability and robustness is ensured by class and instance levels perturbations, respectively.We have performed rigorous experimental evaluations using 10 real gene expression microarray datasets with phenotypes. It revealed that our algorithm outperforms the state-of-the-art algorithms with respect to stability and classification accuracy. We have also done biological enrichment analysis based on gene ontology-biological processes (GO-BP) terms, disease ontology (DO) terms, and biological pathways.

scholarly journals Hybrid Correlation based Gene Selection for Accurate Cancer Classification of Gene Expression Data

2012 ◽  
Vol 43 (14) ◽  
pp. 13-18 ◽  
Author(s):  
Vibhav PrakashSingh ◽  
Singh Gaurav Arvind ◽  
Arindam G Mahapatra
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Gene Selection ◽  
Cancer Classification ◽  
Expression Data ◽  
Selection For

scholarly journals Fine-grained parallelization of fitness functions in bioinformatics optimization problems: gene selection for cancer classification and biclustering of gene expression data

2016 ◽  
Vol 17 (1) ◽  
Author(s):  
Juan A. Gomez-Pulido ◽  
Jose L. Cerrada-Barrios ◽  
Sebastian Trinidad-Amado ◽  
Jose M. Lanza-Gutierrez ◽  
Ramon A. Fernandez-Diaz ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Gene Selection ◽  
Optimization Problems ◽  
Cancer Classification ◽  
Expression Data ◽  
Fine Grained ◽  
Fitness Functions ◽  
Selection For
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