Justin Cosentino

Unsupervised representation learning improves genomic discovery and risk prediction for respiratory and circulatory functions and diseases

This paper introduces a deep learning framework, REGLE, that improves genomic discovery and risk prediction for respiratory and circulatory diseases by learning low-dimensional embeddings from high-dimensional clinical data.

Utilizing multimodal AI to improve genetic analyses of cardiovascular traits

This paper introduces M-REGLE, a multimodal deep learning method that improves genetic analysis of cardiovascular traits by integrating multiple clinical data modalities and outperforms unimodal approaches in locus discovery and risk prediction.

An Empirical Study of ML-based Phenotyping and Denoising for Improved Genomic Discovery

This study evaluates ML-based phenotyping's robustness to label noise using VCDR data, showing improved genetic discovery and PRS predictive power with denoising.

Leveraging deep-learning on raw spirograms to improve genetic understanding and risk scoring of COPD despite noisy labels

This paper trains a deep neural network on noisy COPD labels to predict COPD status from spirograms, improving genetic discovery by identifying new loci and replicating existing ones.