@article{71fc7c4a96da4ac59c0e4ef1cb18f3aa,
title = "A similarity-based approach to leverage multi-cohort medical data on the diagnosis and prognosis of Alzheimer's disease",
abstract = "Motivation: Heterogeneous diseases such as Alzheimer's disease (AD) manifest a variety of phenotypes among populations. Early diagnosis and effective treatment offer cost benefits. Many studies on biochemical and imaging markers have shown potential promise in improving diagnosis, yet establishing quantitative diagnostic criteria for ancillary tests remains challenging. Results: We have developed a similarity-based approach that matches individuals to subjects with similar conditions. We modeled the disease with a Gaussian process, and tested the method in the Alzheimer's Disease Big Data DREAM Challenge. Ranked the highest among submitted methods, our diagnostic model predicted cognitive impairment scores in an independent dataset test with a correlation score of 0.573. It differentiated AD patients from control subjects with an area under the receiver operating curve of 0.920. Without knowing longitudinal information about subjects, the model predicted patients who are vulnerable to conversion from mild-cognitive impairment to AD through the similarity network. This diagnostic framework can be applied to other diseases with clinical heterogeneity, such as Parkinson's disease.",
keywords = "Alzheimer's Disease, Gaussian Process Regression, Kernel method, Machine learning, Patient similarity network",
author = "{The Alzheimer's Disease Neuroimaging Initiative} and Hongjiu Zhang and Fan Zhu and Dodge, {Hiroko H.} and Higgins, {Gerald A.} and Omenn, {Gilbert S.} and Yuanfang Guan",
note = "Funding Information: Data collection and sharing for this project was funded by the Alzheimer{\textquoteright}s Disease Neuroimaging Initiative (ADNI) (National Institutes of Health grant U01 AG024904) and Department of Defense ADNI (award 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{\textquoteright}s Association; Alzheimer{\textquoteright}s Drug Discovery Foundation; Araclon Biotech; Bio-Clinica, Inc.; Biogen; Bristol-Myers Squibb Company; CereSpir, Inc.; Cogstate; Eisai, Inc.; Elan Pharmaceuticals, Inc.; Eli Lilly and Company; EuroImmun; F. Hoffmann-La Roche Ltd and its affiliated company Genentech, Inc.; Fujirebio; GE Healthcare; IXICO, Ltd.; Janssen Alzheimer Immunotherapy Research & Development, LLC; Johnson & Johnson Pharmaceutical Research & Development, LLC; Lumosity; Lundbeck; Merck & Co., Inc.; Meso Scale Diagnostics, LLC; NeuroRx Research; Neurotrack Technologies; Novartis Pharmaceuticals Corporation; Pfizer, Inc.; 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 (www.fnih.org). The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer{\textquoteright}s Therapeutic Research Institute at the University of Southern California. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California. Funding Information: This work was supported by the National Institutes of Health and National Institute of Aging (P30AG053760 to Michigan Alzheimer Disease Core Center), the National Science Foundation (1452656), and the Alzheimer{\textquoteright}s Association [cross-disease brain image modeling]. Publisher Copyright: {\textcopyright} The Author(s) 2018. Published by Oxford University Press.",
year = "2018",
month = jul,
day = "1",
doi = "10.1093/gigascience/giy085",
language = "English (US)",
volume = "7",
journal = "GigaScience",
issn = "2047-217X",
publisher = "BioMed Central",
number = "7",
}