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A non-negative matrix factorization method for detecting modules in heterogeneous omics multi-modal data

机译:一种用于检测异构组学多模态数据中模块的非负矩阵分解方法

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摘要

Motivation: Recent advances in high-throughput omics technologies have enabled biomedical researchers to collect large-scale genomic data. As a consequence, there has been growing interest in developing methods to integrate such data to obtain deeper insights regarding the underlying biological system. A key challenge for integrative studies is the heterogeneity present in the different omics data sources, which makes it difficult to discern the coordinated signal of interest from source-specific noise or extraneous effects.
机译:动机:高通量组学技术的最新进展使生物医学研究人员能够收集大规模的基因组数据。结果,人们对开发集成这些数据以获取有关基础生物系统的更深刻见解的方法的兴趣日益增长。集成研究的一个关键挑战是不同的组学数据源中存在的异质性,这使得很难从特定于源的噪声或外来效应中分辨出感兴趣的协调信号。

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