Neuro-Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, United States.
School of Medicine, Stanford University, Stanford, CA 94305, United States.
Bioinformatics. 2024 Nov 1;40(11). doi: 10.1093/bioinformatics/btae671.
Network analysis (NA) has recently emerged as a new paradigm by which to model the symptom patterns of patients with complex illnesses such as cancer. NA uses graph theory-based methods to capture the interplay between symptoms and identify which symptoms may be most impactful to patient quality of life and are therefore most critical to treat/prevent. Despite NA's increasing popularity in research settings, its clinical applicability is hindered by the lack of a unified platform that consolidates all the software tools needed to perform NA, and by the lack of methods for capturing heterogeneity across patient cohorts. Addressing these limitations, we present PRONA, an R-package for Patient Reported Outcomes Network Analysis. PRONA not only consolidates previous NA tools into a unified, easy-to-use analysis pipeline, but also augments the traditional approach with functionality for performing unsupervised discovery of patient subgroups with distinct symptom patterns.
PRONA is implemented in R. Source code, installation, and use instructions are available on GitHub at https://github.com/bbergsneider/PRONA.
网络分析(NA)最近成为一种新的范例,可以用来模拟癌症等复杂疾病患者的症状模式。NA 使用基于图论的方法来捕捉症状之间的相互作用,并确定哪些症状对患者的生活质量最有影响,因此对治疗/预防最为关键。尽管 NA 在研究环境中的应用越来越广泛,但由于缺乏一个统一的平台来整合执行 NA 所需的所有软件工具,以及缺乏捕获患者队列之间异质性的方法,其临床适用性受到阻碍。为了解决这些限制,我们提出了 PRONA,这是一个用于患者报告结果网络分析的 R 包。PRONA 不仅将以前的 NA 工具整合到一个统一的、易于使用的分析管道中,而且还通过具有用于发现具有不同症状模式的患者亚组的无监督功能来增强传统方法。
PRONA 是用 R 语言实现的。源代码、安装和使用说明可在 GitHub 上获得,网址为 https://github.com/bbergsneider/PRONA。