Abstract Number:
2565
Submission Type:
Contributed Abstract
Contributed Abstract Type:
Paper
Participants:
Steven Broll (1), Sumanta Basu (1), Myung Hee Lee (2), Martin Wells (1)
Institutions:
(1) Cornell University, N/A, (2) Weill Cornell Medicine, N/A
Co-Author(s):
First Author:
Presenting Author:
Abstract Text:
There is a growing interest in longitudinal omics data, but there are gaps in existing high-dimensional methodology. In particular, we are focused on modeling general continuous longitudinal outcomes with continuous longitudinal multi-omics predictors. Simple univariate longitudinal models do not leverage the correlation across predictors, thus losing power. Our method, PROLONG, leverages the first differences of the data to address the piecewise linear structure and the observed time dependence and applies penalties that induce sparsity while incorporating the dependence structure of the data. With automated selection of model hyper-parameters, PROLONG correctly selects target metabolites with high specificity and perfect sensitivity across simulation scenarios. PROLONG selects a set of metabolites from the real data that includes interesting targets identified during EDA.This abstract will review PROLONG and discuss recent extensions to multiple treatment arms and to general multi-omic data.
Keywords:
Omics|Longitudinal|High-dimensional|Biomarkers|TB|Metabolomics
Sponsors:
Biometrics Section
Tracks:
High Dimensional Regression
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