Segmentation and genome annotation (SAGA) algorithms are widely used to understand
genome activity and gene regulation. These algorithms take as input epigenomic datasets …

Despite the wide implementation of machine learning (ML) technique in traffic flow modeling
recently, those data-driven approaches often fall short of accuracy in the cases with a small …

We develop a novel computational method, NucHMM, to identify functional nucleosome
states associated with cell type-specific combinatorial histone marks and nucleosome …

Motivation Segmentation and genome annotation (SAGA) algorithms are widely used to
understand genome activity and gene regulation. These methods take as input a set of …

Motivation The organization of the genome into domains plays a central role in gene
expression and other cellular activities. Researchers identify genomic domains mainly …

Constraints have been shown as an effective way to incorporate unlabeled data for semi-
supervised structured classification. We recognize that, constraints are often conditional and …

The availability of thousands of assays of epigenetic activity necessitates compressed
representations of these data sets that summarize the epigenetic landscape of the genome …

Semi-automated genome annotation (SAGA) methods are widely used to understand
genome activity and gene regulation. These methods take as input a set of sequencing …

In this Ph. D. thesis, we propose frameworks for designing informative position-specific
representations from epigenomic and structural genomic signals. We use recurrent priors in …

Genome annotation methods are widely used to understand the function of the genome. For
example, they can be used to identify the activity of a genomic position that is associated …