TY - JOUR
T1 - Similarity-driven multi-view embeddings from high-dimensional biomedical data
AU - Avants, Brian B.
AU - Tustison, Nicholas J.
AU - Stone, James R.
N1 - Funding Information:
This work is supported by a combined grant from Cohen Veterans Bioscience (CVB-461) and the Office of Naval Research (N00014-18-1-2440) as well as the National Institutes of Health (K01-ES025432-01). Supplementary data used in the preparation of this article were obtained from the PING study database (https://chd.ucsd.edu/research/ping-study.html). The investigators within PING contributed to the design and implementation of the PING database and/or provided data, but did not participate in the analysis or writing of this report. A complete listing of investigators of the PING study can be found at ref. Supplementary data collection and sharing for this project was funded by ADNI (National Institutes of Health Grant U01 AG024904) and the Department of Defense ADNI (Department of Defense award number W81XWH-12-2-0012). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: AbbVie, Alzheimer?s Association; Alzheimer?s Drug Discovery Foundation; Araclon Biotech; BioClinica; Biogen; Bristol Myers Squibb; CereSpir; Cogstate; Eisai; Elan Pharmaceuticals; Eli Lilly and Company; EuroImmun; F. Hoffmann-La Roche and its affiliated company Genentech; Fujirebio; GE Healthcare; IXICO; Janssen Alzheimer Immunotherapy Research & Development; Johnson & Johnson Pharmaceutical Research & Development; Lumosity; Lundbeck; Merck & Co.; Meso Scale Diagnostics; NeuroRx Research; Neurotrack Technologies; Novartis Pharmaceuticals Corporation; Pfizer; Piramal Imaging; Servier; Takeda Pharmaceutical Company; and Transition Therapeutics. The Canadian Institutes of Health Research is providing funds to support ADNI clinical sites in Canada. Private sector contributions are facilitated by the Foundation for the National Institutes of Health (https://fnih.org). The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer?s Therapeutic Research Institute at the University of Southern California. ADNI data are disseminated by the Laboratory of Neuro Imaging at the University of Southern California.
Funding Information:
This work is supported by a combined grant from Cohen Veterans Bioscience (CVB-461) and the Office of Naval Research (N00014-18-1-2440) as well as the National Institutes of Health (K01-ES025432-01).
Publisher Copyright:
© 2021, The Author(s), under exclusive licence to Springer Nature America, Inc. part of Springer Nature.
PY - 2021/2
Y1 - 2021/2
N2 - Diverse, high-dimensional modalities collected in large cohorts present new opportunities for the formulation and testing of integrative scientific hypotheses. Similarity-driven multi-view linear reconstruction (SiMLR) is an algorithm that exploits inter-modality relationships to transform large scientific datasets into smaller, more well-powered and interpretable low-dimensional spaces. SiMLR contributes an objective function to identify joint signal regularization based on sparse matrices representing prior within-modality relationships and an implementation that permits application to joint reduction of large data matrices. We demonstrate that SiMLR outperlforms closely related methods on supervised learning problems in simulation data, a multi-omics cancer survival prediction dataset and multiple modality neuroimaging datasets. Taken together, this collection of results shows that SiMLR may be applied to joint signal estimation from disparate modalities and may yield practically useful results in a variety of application domains.
AB - Diverse, high-dimensional modalities collected in large cohorts present new opportunities for the formulation and testing of integrative scientific hypotheses. Similarity-driven multi-view linear reconstruction (SiMLR) is an algorithm that exploits inter-modality relationships to transform large scientific datasets into smaller, more well-powered and interpretable low-dimensional spaces. SiMLR contributes an objective function to identify joint signal regularization based on sparse matrices representing prior within-modality relationships and an implementation that permits application to joint reduction of large data matrices. We demonstrate that SiMLR outperlforms closely related methods on supervised learning problems in simulation data, a multi-omics cancer survival prediction dataset and multiple modality neuroimaging datasets. Taken together, this collection of results shows that SiMLR may be applied to joint signal estimation from disparate modalities and may yield practically useful results in a variety of application domains.
UR - http://www.scopus.com/inward/record.url?scp=85105444022&partnerID=8YFLogxK
U2 - 10.1038/s43588-021-00029-8
DO - 10.1038/s43588-021-00029-8
M3 - Article
AN - SCOPUS:85105444022
SN - 2662-8457
VL - 1
SP - 143
EP - 152
JO - Nature Computational Science
JF - Nature Computational Science
IS - 2
ER -