Nonparametirc Bayes-based infinite mixture model algorithms for Bioinformatics

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We proposed a non-parametric Bayesan models to two bioinformatics applications: 1) automatic protein function prediction and 2) gene expression network inference. For automatic protein function prediction,we proposed a novel method to predict protein functions, called PreGO. PreGO is an algorithm based on an infinite mixture of hidden Markov models. Given an unannotated protein sequence, PreGO predicts the probability of existence of Gene Ontology terms. For time-varying network inference for gene expression data, we adopted a nonparametric Bayesian regression method to predict interactions between the genes. This method is expected to achieve more flexible regression capability in time-varying network. To obtain stronger robustness to noisy data, we employed the T-Process. The basic algorithm employed reversible jump Markov Chain Monte Carlo for inference of whole network structures. The method can handle (i) change point detection and (ii) network structure inference simultaneously.