Abstract
Studies for the association between diseases and informative single nucleotide polymorphisms (SNPs) have received great attention. However, most of them just use the whole set of useful SNPs and fail to consider the SNP-SNP interactions, while these interactions have already been proven in biology experiments. In this paper, we use a binary particle swarm optimization with hierarchical structure (BPSOHS) algorithm to improve the effective of PSO for the identification of the SNP-SNP interactions. Furthermore, in order to use these SNP interactions in the susceptibility analysis, we propose an emotional neural network (ENN) to treat SNP interactions as emotional tendency. Different from the normal architecture, just as the emotional brain, this architecture provides a specific path to treat the emotional value, by which the SNP interactions can be considered more quickly and directly. The ENN helps us use the prior knowledge about the SNP interactions and other influence factors together. Finally, the experimental results prove that the proposed BPSOHS_ENN algorithm can detect the informative SNP-SNP interaction and predict the breast cancer risk with a much higher accuracy than existing methods.
Highlights
Breast cancer is a major cause of death among women
By designing the emotional value based on the single nucleotide polymorphisms (SNPs) interactions, we explore the range of emotional neural network (ENN) and use it for susceptibility analysis
We propose binary particle swarm optimization with hierarchical structure (BPSOHS) [12] with two kinds of particles: “leader” particles and “follower” particles, and they can be regarded as the “leader” and “followers” in the society
Summary
Breast cancer is a major cause of death among women. Some genes (e.g., BRCA1 and BRCA2) have already been known as the cause of breast cancer [1]. In order to improve the effectiveness to identify these interactions, researchers try to use many different algorithms, for example, [9] uses the Genetic Algorithm (GA), [10] uses the Particle Swarm Optimization (PSO) algorithm, and [11] uses the Polymorphism Interaction Analysis (PIA) These methods have the ability to detect SNP interactions in high dimensional dataset; due to the random generator initial values and optimizing process, they generally need lots of iterations and are trapped into the local optima. Our former work proved that this novel method is faster than other swarm intelligent algorithms and much easier to converge to the globally optimal solution [12] By this improved PSO, after encoding and matching, we can improve the performance of identifying the SNP interactions related to the cancer. The casecontrol study in 10000 people suggests that our pipeline can detect the useful SNP interactions and effectively consider them in susceptibility analysis to breast cancer
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