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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

FIFS: A data mining method for informative marker selection in high dimensional population genomic data.

Ioannis Kavakiotis¹, Patroklos Samaras², Alexandros Triantafyllidis³

¹School of Informatics, Aristotle University of Thessaloniki, 54124, Greece; Department of Genetics, Development and Molecular Biology, School of Biology, Aristotle University of Thessaloniki, 54124, Greece.

Computers in Biology and Medicine

|October 10, 2017

Summary

This summary is machine-generated.

This study introduces Frequent Item Feature Selection (FIFS), a novel data mining method for identifying key genetic markers. FIFS efficiently selects informative Single Nucleotide Polymorphisms (SNPs) for population genomic data analysis.

Keywords:

Ancestry informative marker Big data Bioinformatics Data mining Feature selection Frequent pattern mining Machine learning Population genomics Single nucleotide polymorphism

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Area of Science:

Genomics
Bioinformatics
Data Mining

Background:

Single Nucleotide Polymorphisms (SNPs) are crucial markers in biological analyses with diverse applications.
Classification tasks using genotypes require efficient feature selection, especially with high-dimensional SNP data.
Selecting a minimal set of informative markers is essential for computational and biological efficiency.

Purpose of the Study:

To present a novel data mining approach, FIFS, for selecting informative markers from population genomic data.
To develop a method that efficiently identifies the most discriminative SNPs for classification tasks.
To reduce the number of markers needed for accurate population analysis.

Main Methods:

Proposed a modular data mining approach named FIFS (Frequent Item Feature Selection).
The method identifies frequent and unique genotypes within populations.
It then selects the most appropriate markers to form informative SNP subsets.

Main Results:

FIFS was tested on a real pig breed dataset (446 individuals, 59,436 SNPs).
Achieved >95% assignment accuracy using only 28 SNPs, outperforming other methods.
Required half the number of SNPs compared to state-of-the-art and baseline methods.

Conclusions:

FIFS effectively addresses informative marker selection in high-dimensional genomic datasets.
The approach provides superior results compared to existing methods.
Aids in selecting optimal SNP panels for applications like species identification, wildlife management, and forensics.