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Updated: May 9, 2026

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
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通过BSNMani:贝叶斯斯标量在网络回归与多重学习,将空间奥米克与人口规模的患者表型联系起来

Tong Liu1, Yiwen Yang2, Haowen Wu1

  • 1Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA.

medRxiv : the preprint server for health sciences
|November 24, 2025
PubMed
概括

BSNMani集成空间奥米克数据用于临床预测,优于其他方法. 这种工具揭示了对阿尔茨海默氏症和乳腺癌等疾病的生物学见解,提高了预测准确度.

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Application of Unsupervised Multi-Omic Factor Analysis to Uncover Patterns of Variation and Molecular Processes Linked to Cardiovascular Disease08:51

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