Variational infinite heterogeneous mixture model for semi-supervised clustering of heart enhancers.

ABSTRACT:

Motivation

Mammalian genomes can contain thousands of enhancers but only a subset are actively driving gene expression in a given cellular context. Integrated genomic datasets can be harnessed to predict active enhancers. One challenge in integration of large genomic datasets is the increasing heterogeneity: continuous, binary and discrete features may all be relevant. Coupled with the typically small numbers of training examples, semi-supervised approaches for heterogeneous data are needed; however, current enhancer prediction methods are not designed to handle heterogeneous data in the semi-supervised paradigm.

Results

We implemented a Dirichlet Process Heterogeneous Mixture model that infers Gaussian, Bernoulli and Poisson distributions over features. We derived a novel v

SUBMITTER: Mehdi TF 

PROVIDER: S-EPMC6748727 | biostudies-literature | 2019 Sep

REPOSITORIES: biostudies-literature