Inference of gene networks associated with the host response to infectious disease

Abstract

Inspired by the problem of inferring gene networks associated with the host response to infectious diseases, a new framework for discriminative factor models is developed. Bayesian shrinkage priors are employed to impose (near) sparsity on the factor loadings, while non-parametric techniques are utilized to infer the number of factors needed to represent the data. Two discriminative Bayesian loss functions are investigated, i.e. the logistic log-loss and the max-margin hinge loss. Efficient mean-field variational Bayesian inference and Gibbs sampling are implemented. To address large-scale datasets, an online version of variational Bayes is also developed. Experimental results on two realworld microarray-based gene expression datasets show that the proposed framework achieves comparatively superior classification performance, with model interpretation delivered via pathway association analysis.