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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

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ELSSI: parallel SNP-SNP interactions detection by ensemble multi-type detectors.

Xin Wang^1,2, Xia Cao³, Yuantao Feng³

¹School of Software, Shandong University, Jinan 250101, China.

Briefings in Bioinformatics

|June 13, 2022

Summary

This summary is machine-generated.

Ensemble learning-based single nucleotide polymorphism (SNP)-SNP interactions (SSIs) detection, termed ELSSI, effectively identifies complex disease associations. This approach overcomes limitations of individual detectors for scalable, accurate genome-wide analysis.

Keywords:

SNP–SNP interactions bias divide and conquer ensemble learning multi-type detectors

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Area of Science:

Genetics and Bioinformatics
Computational Biology
Disease Susceptibility Research

Background:

High-throughput genotyping enables single nucleotide polymorphism (SNP)-SNP interactions (SSIs) detection for understanding disease susceptibility.
Existing SSI detection methods struggle with scalability and bias on complex, genome-wide datasets due to reliance on individual detectors.

Purpose of the Study:

To develop a novel ensemble learning-based approach (ELSSI) for scalable and accurate detection of single nucleotide polymorphism (SNP)-SNP interactions (SSIs).
To reduce the bias and computational load associated with individual SSI detection methods.
To enable the detection of high-order SSIs from large genome-wide datasets.

Main Methods:

ELSSI employs an ensemble learning strategy, randomly dividing SNPs into subsets for parallel evaluation by multiple detectors.
A four-stage pipeline (generate, score, switch, filter) iteratively refines SNP combinations for scoring and re-scoring across detectors.
The method is designed for scalability to large genome-wide datasets and flexibility in incorporating new detectors.

Main Results:

ELSSI demonstrated superior efficacy compared to state-of-the-art methods in detecting SSIs across simulated and real genome-wide datasets.
The approach showed particular strength in identifying high-order SSIs.
ELSSI is computationally efficient, runnable on moderate PCs, and accessible online.

Conclusions:

The proposed ELSSI method offers a robust and scalable solution for detecting single nucleotide polymorphism (SNP)-SNP interactions (SSIs), crucial for disease susceptibility studies.
ELSSI effectively mitigates bias and computational challenges inherent in single-detector approaches.
The accessibility and flexibility of ELSSI facilitate its application in genome-wide association studies.