Similarity-driven multi-view embeddings from high-dimensional biomedical data-Reference-Cited by-同舟云学术

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Similarity-driven multi-view embeddings from high-dimensional biomedical data

Published:2021-02 Issue:2 Volume:1 Page:143-152
ISSN:2662-8457
Container-title:Nature Computational Science
language:en
Short-container-title:Nat Comput Sci

Author:

Avants Brian B.^ORCID,Tustison Nicholas J.^ORCID,Stone James R.

Funder

U.S. Department of Health & Human Services | National Institutes of Health

United States Department of Defense | United States Navy | Office of Naval Research

Publisher

Springer Science and Business Media LLC

Subject

General Medicine

Link

http://www.nature.com/articles/s43588-021-00029-8.pdf

Reference81 articles.

1. Cole, J. H., Marioni, R. E., Harris, S. E. & Deary, I. J. Brain age and other bodily ‘ages’: implications for neuropsychiatry. Mol. Psychiatry 24, 266–281 (2019).

2. Wray, N. R. et al. Genome-wide association analyses identify 44 risk variants and refine the genetic architecture of major depression. Nat. Genet. 50, 668–681 (2018).

3. Habeck, C., Stern, Y. & Alzheimer’s Disease Neuroimaging Initiative. Multivariate data analysis for neuroimaging data: overview and application to Alzheimer’s disease. Cell Biochem. Biophys. 58, 53–67 (2010).

4. Shamy, J. L. et al. Volumetric correlates of spatiotemporal working and recognition memory impairment in aged rhesus monkeys. Cereb. Cortex 21, 1559–1573 (2011).

5. McKeown, M. J. et al. Analysis of fMRI data by blind separation into independent spatial components. Hum. Brain Mapp. 6, 160–188 (1998).

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