ArticleGenes & genomics2020
Detecting biomarkers from microarray data using distributed correlation based gene selection.
Article in Genes & genomics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Navigating the microarray landscape: a comprehensive review of feature selection techniques and their applications.Frontiers in big data · 2025Review
- B-Cell Receptor Signaling Is Thought to Be a Bridge between Primary Sjogren Syndrome and Diffuse Large B-Cell Lymphoma.International journal of molecular sciences · 2023Article
- Multiple Criteria Optimization (MCO): A gene selection deterministic tool in RStudio.PloS one · 2022Article
- Framework for classification of cancer gene expression data using Bayesian hyper-parameter optimization.Medical & biological engineering & computing · 2021Article
- Brain Cancer Prediction Based on Novel Interpretable Ensemble Gene Selection Algorithm and Classifier.Diagnostics (Basel, Switzerland) · 2021Article
- Article
- An Efficient Cancer Classification Model Using Microarray and High-Dimensional Data.Computational intelligence and neuroscience · 2021Article
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2 authors.
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Abstract
backgroundOver the past few decades, DNA microarray technology has emerged as a prevailing process for early identification of cancer subtypes. Several feature selection (FS) techniques have been widely applied for identifying cancer from microarray gene data but only very few studies have been conducted on distributing the feature selection process for detecting cancer subtypes.
objectiveNot all the gene expressions are needed in prediction, this research article objective is to select discriminative biomarkers by using distributed FS method which helps in accurately diagnosis of cancer subtype. Traditional feature selection techniques have several drawbacks like unrelated features that could perform well in terms of classification accuracy with a suitable subset of genes will be left out of the selection.
methodTo overcome the issue, in this paper a new filter-based method for gene selection is introduced which can select the highly relevant genes for distinguishing tissues from the gene expression dataset. In addition, it is used to compute the relation between gene-gene and gene-class and simultaneously identify subset of essential genes. Our method is tested on Diffuse Large B cell Lymphoma (DLBCL) dataset by using well-known classification techniques such as support vector machine, naïve Bayes, k-nearest neighbor, and decision tree.
resultsResults on biological DLBCL dataset demonstrate that the proposed method provides promising tools for the prediction of cancer type, with the prediction accuracy of 97.62%, precision of 94.23%, sensitivity of 94.12%, F-measure of 90.12%, and ROC value of 99.75%.
conclusionThe experimental results reveal the fact that the proposed method is significantly improved classification accuracy and execution time, compared to existing standard algorithms when applied to the non-partitioned dataset. Furthermore, the extracted genes are biologically sound and agree with the outcome of relevant biomedical studies.
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