Multiobjective differential evolution-based multifactor dimensionality reduction for detecting gene–gene interactions

Abstract

Epistasis within disease-related genes (gene–gene interactions) was determined through contingency table measures based on multifactor dimensionality reduction (MDR) using single-nucleotide polymorphisms (SNPs). Most MDR-based methods use the single contingency table measure to detect gene–gene interactions; however, some gene–gene interactions may require identification through multiple contingency table measures. In this study, a multiobjective differential evolution method (called MODEMDR) was proposed to merge the various contingency table measures based on MDR to detect significant gene–gene interactions. Two contingency table measures, namely the correct classification rate and normalized mutual information, were selected to design the fitness functions in MODEMDR. The characteristics of multiobjective optimization enable MODEMDR to use multiple measures to efficiently and synchronously detect significant gene–gene interactions within a reasonable time frame. Epistatic models with and without marginal effects under various parameter settings (heritability and minor allele frequencies) were used to assess existing methods by comparing the detection success rates of gene–gene interactions. The results of the simulation datasets show that MODEMDR is superior to existing methods. Moreover, a large dataset obtained from the Wellcome Trust Case Control Consortium was used to assess MODEMDR. MODEMDR exhibited efficiency in identifying significant gene–gene interactions in genome-wide association studies.

A Survey on Swarm Intelligence Search Methods Dedicated to Detection of High-Order SNP Interactions

“…Compared with the GA, the DE algorithm is a real-value-coded algorithm that solves continuous optimization problems very well; however, for discrete combinational optimization problems, a modification of the evolution strategy or bound constraints is required. Yang et al proposed two DE algorithms (MODEMDR 2017 and CT-BDE 2018) [45], [46] for detecting gene-gene interactions. In MODEMDR, the n-order SNP combination is represented using an n-digit decimal code, and two MDR-based contingency table measures (CCR and NMI) are utilized as the fitness functions.…”

Deep Learning for Intradialytic Hypotension Prediction in Hemodialysis Patients

“…MDR has been successfully applied to detect interaction effect in hypertension [15], coronary artery disease [16] and breast cancer [17]. MDR have been improved and applied in various biomedicine topic recently [16], [18]- [22]. The interaction analysis of this study is inspired from MDR that pooled the multi-level characteristics of clinical factors into high-risk and low-risk groups to identify high-risk interaction model of rapid IDH.…”

FCTP-WSRC: Protein–Protein Interactions Prediction via Weighted Sparse Representation Based Classification

“…What's more, several evolutionary based methods are proposed for dimensionality reduction (Chuang et al, 2016). A multi-objective differential evolution method (called MODEMDR) was proposed to merge the various contingency table measures based on MDR to detect significant gene-gene interactions (Yang et al, 2017). In this paper, principal component analysis (PCA) is utilized to do the feature extraction which projects the original feature space into a new space.…”