The integration of multiple heterogeneous data into graph models has been the subject of
extensive research in recent years. Harnessing these resulting Heterogeneous Information …

Patients' waiting time is a major issue in the Canadian healthcare system. The planning for
resource allocation impacts patients' waiting time in medicare settings. This research …

Cancer drug response prediction is a crucial task in precision medicine, but existing models
have limitations in effectively representing molecular profiles of cancer cells. Specifically …

Motivation Dysregulation of a gene's function, either due to mutations or impairments in
regulatory networks, often triggers pathological states in the affected tissue. Comprehensive …

High-dimensional gene expression data cause challenges for traditional statistical tools,
particularly when dealing with non-linear relationships and outliers. The present study …

This work presents DeepReGraph, a novel method for co-clustering genes and cis-
regulatory elements (CREs) into candidate regulatory networks. Gene expression data, as …

Existing unsupervised methods are often unable to capture accurate 3D shapes due to the
ambiguity of shapes and albedo maps, limiting their applicability to downstream tasks …

This research aimed to investigate the synergies between deep learning and
heterogeneous graph-based scenario modeling. The candidate has thoroughly studied the …