Single-cell transcriptomics provides an opportunity to characterize cell-type-specific
transcriptional networks, intercellular signaling pathways and cellular diversity with …

Uncovering modular structure in networks is fundamental for systems in biology, physics,
and engineering. Community detection identifies candidate modules as hypotheses, which …

Background Assigning every human gene to specific functions, diseases and traits is a
grand challenge in modern genetics. Key to addressing this challenge are computational …

Disease modules in molecular interaction maps have been useful for characterizing
diseases. Yet biological networks, that commonly define such modules are incomplete and …

Biological networks catalog the complex web of interactions happening between different
molecules, typically proteins, within a cell. These networks are known to be highly modular …

We present Knowledge Engine for Genomics (KnowEnG), a free-to-use computational
system for analysis of genomics data sets, designed to accelerate biomedical discovery. It …

Background Assistive automatic seizure detection can empower human annotators to
shorten patient monitoring data review times. We present a proof-of-concept for a seizure …

We define a disease module as a partition of a molecular network whose components are
jointly associated with one or several diseases or risk factors thereof. Identification of such …

PyGenePlexus is a Python package that enables a user to gain insight into any gene set of
interest through a molecular interaction network informed supervised machine learning …

The identification of communities, or modules, is a common operation in the analysis of large
biological networks. The Disease Module Identification DREAM challenge established a …