A support vector machine approach for detecting gene‐gene interaction

Abstract

Although genetic factors play an important role in most human diseases, multiple genes or genes and environmental factors may influence individual risk. In order to understand the underlying biological mechanisms of complex diseases, it is important to understand the complex relationships that control the process. In this paper, we consider different perspectives, from each optimization, complexity analysis, and algorithmic design, which allows us to describe a reasonable and applicable computational framework for detecting gene-gene interactions. Accordingly, support vector machine and combinatorial optimization techniques (local search and genetic algorithm) were tailored to fit within this framework. Although the proposed approach is computationally expensive, our results indicate this is a promising tool for the identification and characterization of high order gene-gene and gene-environment interactions. We have demonstrated several advantages of this method, including the strong power for classification, less concern for overfitting, and the ability to handle unbalanced data and achieve more stable models. We would like to make the support vector machine and combinatorial optimization techniques more accessible to genetic epidemiologists, and to promote the use and extension of these powerful approaches.

Reconstructability Analysis of Epistasis

“…RA and other nominal data methods are inherently more appropriate to studying genomic data than other approaches such as neural nets or support vector machines (Chen et al, 2008) that presuppose metric information. The predictive relation in an RA (or LL, LR, or BN) model is precisely the conditional probability of the discrete output, given the discrete inputs.…”

Machine learning approaches for the prediction of obesity using publicly available genetic profiles

“…A total of 13 SNPs associated with obesity and T2D related traits, and prostate cancer are used as inputs for our prediction. Seven machine learning models simulated for the prediction of obesity were used, including: gradient boosting [43], generalised linear model [44], classification trees [45], k-nearest neighbours (KNN) [46], support vector machine (SVM) [47], random forest (RF) [48] and multilayer perceptron (MLP) neural network [49] trained using backpropagation.…”

Reconstructability analysis of genetic loci associated with Alzheimer disease

“…(They can also be applied to quantitative or ordinal variables by binning.) By contrast, certain other machine learning methods such as neural nets [2], [28] or support vector machines [29], presuppose metric information and are thus less inherently suited for genomic analyses.…”