高通量测序数据的可获得性为全面了解人类疾病提供了机会，同时也为使用如此高维数据进行机器学习模型的训练带来了挑战。在这里，我们提出了一个去噪多组学整合框架，该框架包含了一个分布特征去噪算法，即基于分布的特征选择（FSD），用于降维，并且一个多组学整合框架，即注意力多组学整合（AttentionMOI），用于预测癌症预后和鉴定癌症亚型。我们证明了FSD在单组学数据或多组学数据上都可以改善模型性能，用于15个癌症类型的肿瘤基因组图谱计划（TCGA）中的生存预测和肾癌亚型识别。而我们的整合框架AttentionMOI在特征维度高的情况下，优于机器学习模型和当前的多组学整合算法。此外，FSD还能够识别与癌症预后相关的特征，并可以被考虑为生物标志物。© 作者 2023. 版权所有，由牛津大学出版社出版。如需授权，请发送电子邮件至: journals.permissions@oup.com.

The availability of high-throughput sequencing data creates opportunities to comprehensively understand human diseases as well as challenges to train machine learning models using such high dimensions of data. Here, we propose a denoised multi-omics integration framework, which contains a distribution-based feature denoising algorithm, Feature Selection with Distribution (FSD), for dimension reduction and a multi-omics integration framework, Attention Multi-Omics Integration (AttentionMOI) to predict cancer prognosis and identify cancer subtypes. We demonstrated that FSD improved model performance either using single omic data or multi-omics data in 15 The Cancer Genome Atlas Program (TCGA) cancers for survival prediction and kidney cancer subtype identification. And our integration framework AttentionMOI outperformed machine learning models and current multi-omics integration algorithms with high dimensions of features. Furthermore, FSD identified features that were associated to cancer prognosis and could be considered as biomarkers.© The Author(s) 2023. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.