一种将多模态组学数据整合到稀疏且可解释模型中的方法。
An approach for integrating multimodal omics data into sparse and interpretable models.
机构信息
Lausanne University Hospital and University of Lausanne, Swiss Institute of Bioinformatics, Lausanne, Switzerland.
Lausanne University Hospital and University of Lausanne, Swiss Institute of Bioinformatics, Lausanne, Switzerland.
出版信息
Cell Rep Methods. 2024 Feb 26;4(2):100718. doi: 10.1016/j.crmeth.2024.100718.
Abstract
Using omics data, a common goal is to identify a concise set of variables that predict a clinical endpoint from an extensive pool. In a recent paper published in Nature Biotechnology, Hédou et al. introduced Stabl, a computational method crafted to identify sparse yet robust signatures linked to endpoints.
摘要
利用组学数据，一个常见的目标是从大量数据中确定一组简洁的变量来预测临床终点。在最近发表在《自然生物技术》杂志上的一篇论文中，Hédou 等人介绍了 Stabl，这是一种计算方法，旨在识别与终点相关的稀疏但稳健的特征。
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