Reliability and predictability of phenotype information from functional connectivity in large imaging datasets

no code implementations • 1 May 2024 • Jessica Dafflon, Dustin Moraczewski, Eric Earl, Dylan M. Nielson, Gabriel Loewinger, Patrick McClure, Adam G. Thomas, Francisco Pereira

In this study, we trained models that predict observable individual traits (phenotypes) and their corresponding singular value decomposition (SVD) representations - herein referred to as latent phenotypes from resting state functional connectivity.

Multi-Task Learning for Sparsity Pattern Heterogeneity: A Discrete Optimization Approach

1 code implementation • 16 Dec 2022 • Gabriel Loewinger, Kayhan Behdin, Kenneth T. Kishida, Giovanni Parmigiani, Rahul Mazumder

Allowing the regression coefficients of tasks to have different sparsity patterns (i. e., different supports), we propose a modeling framework for MTL that encourages models to share information across tasks, for a given covariate, through separately 1) shrinking the coefficient supports together, and/or 2) shrinking the coefficient values together.

Multi-Task Learning Variable Selection

Optimal Ensemble Construction for Multi-Study Prediction with Applications to COVID-19 Excess Mortality Estimation

1 code implementation • 19 Sep 2021 • Gabriel Loewinger, Rolando Acosta Nunez, Rahul Mazumder, Giovanni Parmigiani

Importantly, our approach outperforms multi-study stacking and other standard methods in this application.

Mortality Prediction