Bibliographic Details
| Title: |
A robust and stable gene selection algorithm based on graph theory and machine learning |
| Authors: |
Subrata Saha, Ahmed Soliman, Sanguthevar Rajasekaran |
| Source: |
Human Genomics, Vol 15, Iss 1, Pp 1-16 (2021) |
| Publisher Information: |
BMC, 2021. |
| Publication Year: |
2021 |
| Collection: |
LCC:Medicine
LCC:Genetics |
| Subject Terms: |
Robust and Stable Gene Selection Algorithm (RSGSA), Symmetric Uncertainty (SU), Gain ratio (GR), Support vector machine-recursive feature elimination (SVM-RFE), Linear Support Vector Machine (LSVM), Medicine, Genetics, QH426-470 |
| More Details: |
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. |
| Document Type: |
article |
| File Description: |
electronic resource |
| Language: |
English |
| ISSN: |
1479-7364 |
| Relation: |
https://doaj.org/toc/1479-7364 |
| DOI: |
10.1186/s40246-021-00366-9 |
| Access URL: |
https://doaj.org/article/21e4f9da095a4dce8150adf6a5967f79 |
| Accession Number: |
edsdoj.21e4f9da095a4dce8150adf6a5967f79 |
| Database: |
Directory of Open Access Journals |
