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Multiple Criteria Optimization (MCO): A gene selection deterministic tool in RStudio.

Isis Narváez-Bandera1, Deiver Suárez-Gómez1, Clara E Isaza1,2,3,4

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Summary
This summary is machine-generated.

This study introduces an open-source R tool for objective and repeatable gene selection using multiple criteria optimization (MCO). The tool identifies potential Parkinson's disease biomarkers, MMP9 and TUBB2A, from microarray data analysis.

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Area of Science:

  • Bioinformatics
  • Genomics
  • Computational Biology

Background:

  • Gene selection is crucial for understanding molecular mechanisms and therapeutic development.
  • Reproducibility, objectivity, and repeatability are essential in biological and informatics analyses.
  • Previous work proposed a multiple criteria optimization (MCO) algorithm for objective gene selection from microarray data.

Purpose of the Study:

  • To develop an open-source R tool for gene selection using MCO.
  • To enable both individual dataset analysis and meta-analysis of microarray data.
  • To provide an affordable, repeatable, and objective method for detecting differentially expressed genes.

Main Methods:

  • Development of an open-source R tool implementing the MCO algorithm.
  • The tool supports individual analysis of datasets (2-3 performance measures) and meta-analysis (up to 5 datasets).
  • Demonstration using four Parkinson's Disease (PD) microarray datasets for individual and meta-analysis.

Main Results:

  • The MCO algorithm ensures objective and repeatable gene selection without user parameter manipulation.
  • The R tool is portable, requires modest hardware, and is license-free.
  • Analysis identified MMP9 and TUBB2A as potential PD genetic biomarkers due to their consistent presence across datasets.

Conclusions:

  • The developed R tool offers an affordable, repeatable, and objective approach to gene selection from microarrays.
  • The identified genes MMP9 and TUBB2A show potential as PD biomarkers, validated by literature.
  • The tool is applicable to various array-based experiments, including microRNA and protein arrays.