可复用性报告：多组学网络推断的稳健性取决于先验知识与数据可得性
Reusability report: multi-sample omics network inference operates in distinct robustness regimes defined by prior knowledge and data availability
摘要
Nature Machine Intelligence 发表的可复用性报告考察了此前提出的生物网络学习框架 CORNETO 的可复用性，发现稳健的网络推断取决于数据可得性、先验知识、正则化和生物学背景，并据此划分出不同的稳健性区间。原文链接：https://www.nature.com/articles/s42256-026-01311-y
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正文 · 原文
Nature Machine Intelligence, Published online: 19 October 2026; doi:10.1038/s42256-026-01311-y
This work studies the reusability of CORNETO, a previously developed framework for learning biological networks from multi-omics data. Robust network inference is found to depend on data availability, prior knowledge, regularization and biological context.
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来源：Nature Machine Intelligence · nature.com
引用原文
Xabier Martínez-de-Morentin. Reusability report: multi-sample omics network inference operates in distinct robustness regimes defined by prior knowledge and data availability[EB/OL]. (2026-10-19)[访问日期]. https://www.nature.com/articles/s42256-026-01311-y.