Article: Autoencoder with salp optimisation technique for exploring SNP-SNP interactions in Alzheimer's disease Journal: International Journal of Intelligent Engineering Informatics (IJIEI) 2025 Vol.13 No.1 pp.55 - 77 Abstract: Genetic association research aims to identify genetic variations linked to specific disease states or conditions. Genetic interactions, also known as epistasis, typically involve interactions among numerous single nucleotide polymorphisms (SNPs). Detecting genetic interactions among millions of SNPs in GWAS is challenging. This study presents a two-stage epistasis model called autoencoder based feature selection with Salp optimiser for Epistasis identification (AE-SalpEpi). The autoencoder (AE) approach is designed in the screen phase to pick a subset of features having a high association with Alzheimer's disease. During the selection stage, disease-correlated SNP combinations are chosen using SalpEpi-SO and SalpEpi-MO. The simulated and real Alzheimer's disease dataset are used to quantify the performance of AE-SalpEpi-SO and AE-SalpEpi-MO and also contrasted to state-of-art techniques such as MCASO-Epi and MACOED. Experimental results showed that the suggested methods successfully identified interactions with the APOE gene variation, a known risk factor for Alzheimer's disease. Inderscience Publishers - linking academia, business and industry through research

Title: Autoencoder with salp optimisation technique for exploring SNP-SNP interactions in Alzheimer's disease

Authors: S. Priya; R. Manavalan

Addresses: Department of Information Technology, Arignar Anna Government Arts College, Villupuram – 605 602, India; Affiliated to Annamalai University, Chidambaram, Tamilnadu, India ' Department of Computer Science, Arignar Anna Government Arts College, Villupuram – 605 602, India; Affiliated to Annamalai University, Chidambaram, Tamilnadu, India

Abstract: Genetic association research aims to identify genetic variations linked to specific disease states or conditions. Genetic interactions, also known as epistasis, typically involve interactions among numerous single nucleotide polymorphisms (SNPs). Detecting genetic interactions among millions of SNPs in GWAS is challenging. This study presents a two-stage epistasis model called autoencoder based feature selection with Salp optimiser for Epistasis identification (AE-SalpEpi). The autoencoder (AE) approach is designed in the screen phase to pick a subset of features having a high association with Alzheimer's disease. During the selection stage, disease-correlated SNP combinations are chosen using SalpEpi-SO and SalpEpi-MO. The simulated and real Alzheimer's disease dataset are used to quantify the performance of AE-SalpEpi-SO and AE-SalpEpi-MO and also contrasted to state-of-art techniques such as MCASO-Epi and MACOED. Experimental results showed that the suggested methods successfully identified interactions with the APOE gene variation, a known risk factor for Alzheimer's disease.

Keywords: epistasis; feature selection; diseases; single nucleotide polymorphism; SNPs; optimisation; multi-objective; Alzheimer's disease; complex diseases.

DOI: 10.1504/IJIEI.2025.144271

International Journal of Intelligent Engineering Informatics, 2025 Vol.13 No.1, pp.55 - 77

Received: 11 Aug 2023
Accepted: 08 Jun 2024
Published online: 04 Feb 2025 *

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Title: Autoencoder with salp optimisation technique for exploring SNP-SNP interactions in Alzheimer's disease

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