NetMedPy：一个用于大规模网络医学筛查的Python软件包。

NetMedPy: a Python package for large-scale network medicine screening.

作者信息

Aldana Andres, Sebek Michael, Ispirova Gordana, Dorantes-Gilardi Rodrigo, Loscalzo Joseph, Barabási Albert-László, Menichetti Giulia

机构信息

Network Science Institute, Northeastern University, 177 Huntington Avenue, Boston, MA 02115, USA.

Channing Division of Network Medicine, Brigham and Women's Hospital, Harvard Medical School, 181 Longwood Avenue, Boston, MA 02115, USA.

出版信息

Bioinformatics. 2025 Sep 1;41(9). doi: 10.1093/bioinformatics/btaf338.

DOI:10.1093/bioinformatics/btaf338

PMID:40574656

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC12401583/

Abstract

SUMMARY

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.

AVAILABILITY AND IMPLEMENTATION

NetMedPy is an open-source Python package under an MIT license. Source code, documentation, and installation instructions can be downloaded from https://github.com/menicgiulia/NetMedPy and https://pypi.org/project/NetMedPy. The package can run on any standard desktop computer or computing cluster.

摘要

网络医学利用亚细胞网络内信息流的量化来阐明疾病病因和共病情况，以及预测药物疗效并识别潜在治疗靶点。然而，当前的网络医学工具集通常缺乏计算效率高的数据处理管道，无法支持多种评分函数、网络距离度量和空模型。这些限制阻碍了它们在大规模分子筛选、假设检验和集成建模中的应用。为应对这些挑战，我们引入了NetMedPy，这是一个专为全面的网络医学分析而设计的高效且通用的计算包。