  Precision medicine aims for personalized prognosis and therapeutics by
utilizing recent genome-scale high-throughput profiling techniques, including
next-generation sequencing (NGS). However, translating NGS data faces several
challenges. First, NGS count data are often overdispersed, requiring
appropriate modeling. Second, compared to the number of involved molecules and
system complexity, the number of available samples for studying complex
disease, such as cancer, is often limited, especially considering disease
heterogeneity. The key question is whether we may integrate available data from
all different sources or domains to achieve reproducible disease prognosis
based on NGS count data. In this paper, we develop a Bayesian Multi-Domain
Learning (BMDL) model that derives domain-dependent latent representations of
overdispersed count data based on hierarchical negative binomial factorization
for accurate cancer subtyping even if the number of samples for a specific
cancer type is small. Experimental results from both our simulated and NGS
datasets from The Cancer Genome Atlas (TCGA) demonstrate the promising
potential of BMDL for effective multi-domain learning without "negative
transfer" effects often seen in existing multi-task learning and transfer
learning methods.
