Abstract
AbstractSummaryNetwork medicine leverages the quantification of information flow within sub-cellular networks to elucidate disease etiology and comorbidity, as well as to predict drug efficacy and identify potential therapeutic targets. However, current Network Medicine toolsets often lack computationally efficient data processing pipelines that support diverse scoring functions, network distance metrics, and null models. These limitations hamper their application in large-scale molecular screening, hypothesis testing, and ensemble modeling. To address these challenges, we introduce NetMedPy, a highly efficient and versatile computational package designed for comprehensive Network Medicine analyses.AvailabilityNetMedPy is an open-source Python package under an MIT license. Source code, documentation, and installation instructions can be downloaded fromhttps://github.com/menicgiulia/NetMedPyandhttps://pypi.org/project/NetMedPy. The package can run on any standard desktop computer or computing cluster.
Publisher
Cold Spring Harbor Laboratory