2018/10/22 by Ehsan Hajiramezanali, Hajiramezanali, Ehsan, Siamak Zamani Dadaneh +7 · 1 citation
Biochemistry, Genetics and Molecular Biology · #Applications (stat.AP) #Biomedical Text Mining and Ontologies #FOS: Biological sciences #FOS: Computer and information sciences #Gene expression and cancer classification #Genomics (q-bio.GN) #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Machine Learning in Bioinformatics
paper · pdf · doi:10.48550/arxiv.1810.09433
openalex publication_date 2018/10/22 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.