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Related Concept Videos

DNA Microarrays02:34

DNA Microarrays

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Microarrays are high-throughput and relatively inexpensive assays that can be automated to analyze large quantities of data at a time. They are used in genome-wide studies to compare gene or protein expression under two varied conditions, such as healthy and diseased states. Microarrays consist of glass or silica slides on which probe molecules are covalently attached through surface functionalization. Most commonly, the slides are prepared through the chemisorption of silanes to silica...
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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
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Detecting biomarkers from microarray data using distributed correlation based gene selection.

Alok Kumar Shukla1, Diwakar Tripathi2

  • 1Department of Computer Science and Engineering, G L Bajaj Institute of Technology and Management, Greater Noida, Uttar Pradesh, India. alokjestshukla@gmail.com.

Genes & Genomics
|February 11, 2020
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Summary
This summary is machine-generated.

This study introduces a novel distributed feature selection method for identifying cancer subtypes from gene expression data. The new approach enhances diagnostic accuracy and efficiency in cancer subtyping using DNA microarrays.

Keywords:
DLBCLFeature selectionInformation theorySpearman’s correlation

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Area of Science:

  • Bioinformatics
  • Genomics
  • Cancer Research

Background:

  • DNA microarray technology is crucial for early cancer subtype identification.
  • Traditional feature selection methods for microarray data have limitations in identifying discriminative cancer biomarkers.
  • Few studies have focused on distributed feature selection for cancer subtyping.

Purpose of the Study:

  • To develop a distributed feature selection (FS) method for identifying discriminative biomarkers for accurate cancer subtype diagnosis.
  • To address the drawbacks of traditional FS techniques that may exclude relevant genes.

Main Methods:

  • A novel filter-based method for gene selection was introduced to identify highly relevant genes.
  • The method computes gene-gene and gene-class relationships to identify essential gene subsets.
  • The approach was tested on a Diffuse Large B cell Lymphoma (DLBCL) dataset using various classification techniques.

Main Results:

  • The proposed method achieved high prediction accuracy (97.62%) on the DLBCL dataset.
  • Performance metrics included precision (94.23%), sensitivity (94.12%), F-measure (90.12%), and ROC value (99.75%).
  • The method demonstrated improved classification accuracy and execution time compared to standard algorithms.

Conclusions:

  • The developed method offers a promising tool for cancer subtyping and prediction.
  • Extracted genes are biologically relevant and consistent with existing biomedical research.
  • The distributed FS approach enhances efficiency and accuracy in analyzing gene expression data for cancer diagnosis.