title: Joint association discovery and diagnosis of Alzheimer's disease by supervised heterogeneous multiview learning.
abstract: A key step for Alzheimer's disease (AD) study is to identify associations between genetic variations and intermediate phenotypes (e.g., brain structures). At the same time, it is crucial to develop a noninvasive means for AD diagnosis. Although these two tasks-association discovery and disease diagnosis-have been treated separately by a variety of approaches, they are tightly coupled due to their common biological basis. We hypothesize that the two tasks can potentially benefit each other by a joint analysis, because (i) the association study discovers correlated biomarkers from different data sources, which may help improve diagnosis accuracy, and (ii) the disease status may help identify disease-sensitive associations between genetic variations and MRI features. Based on this hypothesis, we present a new sparse Bayesian approach for joint association study and disease diagnosis. In this approach, common latent features are extracted from different data sources based on sparse projection matrices and used to predict multiple disease severity levels based on Gaussian process ordinal regression; in return, the disease status is used to guide the discovery of relationships between the data sources. The sparse projection matrices not only reveal the associations but also select groups of biomarkers related to AD. To learn the model from data, we develop an efficient variational expectation maximization algorithm. Simulation results demonstrate that our approach achieves higher accuracy in both predicting ordinal labels and discovering associations between data sources than alternative methods. We apply our approach to an imaging genetics dataset of AD. Our joint analysis approach not only identifies meaningful and interesting associations between genetic variations, brain structures, and AD status, but also achieves significantly higher accuracy for predicting ordinal AD stages than the competing methods.
authors.affiliation: Department of Computer Science, Purdue University, USA. szhe@purdue.edu.
authors.forename: Shandian
authors.initials: S
authors.lastname: Zhe
authors.forename: Zenglin
authors.initials: Z
authors.lastname: Xu
authors.forename: Yuan
authors.initials: Y
authors.lastname: Qi
authors.forename: Peng
authors.initials: P
authors.lastname: Yu
mesh_terms: D000465:Algorithms; D000544:Alzheimer Disease / Q000175:diagnosis* / Q000235:genetics*; D001185:Artificial Intelligence; D001499:Bayes Theorem; D015415:Biomarkers; D001921:Brain / Q000473:pathology; D019295:Computational Biology; D003198:Computer Simulation; D030541:Databases, Genetic / Q000706:statistics & numerical data; D003936:Diagnosis, Computer-Assisted / Q000706:statistics & numerical data; D056726:Genetic Association Studies / Q000706:statistics & numerical data; D006801:Humans; D008279:Magnetic Resonance Imaging; D015233:Models, Statistical; D016011:Normal Distribution; D010641:Phenotype; D057285:Precision Medicine
chemical_list: D015415:Biomarkers