{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"submitter":["Aldana A"],"funding":["NHLBI NIH HHS"],"pubmed_abstract":["Network 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."],"journal":["bioRxiv : the preprint server for biology"],"pagination":["2024.09.05.611537"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC11577252"],"repository":["biostudies-literature"],"pubmed_title":["NetMedPy: A Python package for Large-Scale Network Medicine Screening."],"pmcid":["PMC11577252"],"funding_grant_id":["R01 HL166137","R01 HL155096","R01 HL155107","K25 HL173665"],"pubmed_authors":["Aldana A","Sebek M","Loscalzo J","Dorantes-Gilardi R","Barabasi AL","Menichetti G","Ispirova G"],"additional_accession":[]},"is_claimable":false,"name":"NetMedPy: A Python package for Large-Scale Network Medicine Screening.","description":"Network 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.","dates":{"release":"2024-01-01T00:00:00Z","publication":"2024 Sep","modification":"2026-04-08T14:57:37.938Z","creation":"2026-04-08T03:52:32.937Z"},"accession":"S-EPMC11577252","cross_references":{"pubmed":["39569139"],"doi":["10.1101/2024.09.05.611537"]}}