Faculty, Staff and Student Publications
Publication Date
5-23-2024
Journal
Briefings in Bioinformatics
Abstract
In precision medicine, both predicting the disease susceptibility of an individual and forecasting its disease-free survival are areas of key research. Besides the classical epidemiological predictor variables, data from multiple (omic) platforms are increasingly available. To integrate this wealth of information, we propose new methodology to combine both cooperative learning, a recent approach to leverage the predictive power of several datasets, and polygenic hazard score models. Polygenic hazard score models provide a practitioner with a more differentiated view of the predicted disease-free survival than the one given by merely a point estimate, for instance computed with a polygenic risk score. Our aim is to leverage the advantages of cooperative learning for the computation of polygenic hazard score models via Cox's proportional hazard model, thereby improving the prediction of the disease-free survival. In our experimental study, we apply our methodology to forecast the disease-free survival for Alzheimer's disease (AD) using three layers of data. One layer contains epidemiological variables such as sex, APOE (apolipoprotein E, a genetic risk factor for AD) status and 10 leading principal components. Another layer contains selected genomic loci, and the last layer contains methylation data for selected CpG sites. We demonstrate that the survival curves computed via cooperative learning yield an AUC of around $0.7$, above the state-of-the-art performance of its competitors. Importantly, the proposed methodology returns (1) a linear score that can be easily interpreted (in contrast to machine learning approaches), and (2) a weighting of the predictive power of the involved data layers, allowing for an assessment of the importance of each omic (or other) platform. Similarly to polygenic hazard score models, our methodology also allows one to compute individual survival curves for each patient.
Keywords
Humans, Precision Medicine, Alzheimer Disease, Disease-Free Survival, Machine Learning, Proportional Hazards Models, Multifactorial Inheritance, Male, Female, Multiomics, Alzheimer, cooperative learning, Cox proportional hazard, lasso, penalized regression, precision medicine, survival
DOI
10.1093/bib/bbae267
PMID
38836403
PMCID
PMC11151121
PubMedCentral® Posted Date
6-5-2024
PubMedCentral® Full Text Version
Post-print
Published Open-Access
yes
Included in
Biomedical Informatics Commons, Cognition and Perception Commons, Health Psychology Commons, Mental and Social Health Commons, Neurology Commons, Public Health Commons
