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Integrative machine learning approaches for predicting disease risk using multi-omics data from the UK Biobank

Medicine and Health

Integrative machine learning approaches for predicting disease risk using multi-omics data from the UK Biobank

O. Aguilar, C. Chang, et al.

Explore how Oscar Aguilar, Cheng Chang, Elsa Bismuth, and Manuel A Rivas harness machine learning to analyze multi-omics data from the UK Biobank, unveiling enhanced disease risk prediction for 22 conditions. Discover the surprising impact of integrating diverse biological data.

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Playback language: English
Abstract
This study investigates the use of integrative machine learning approaches to predict disease risk using multi-omics data from the UK Biobank. The researchers trained prediction and survival models using demographic, genomic, metabolomic, and clinical biomarker data for 22 diseases. They compared several machine learning models (Lasso Regression, Multi-Layer Perceptron, XG Boost, and ADA Boost) and found that integrating multi-omics data improved risk prediction for 8 diseases. However, the contribution of metabolomic data was marginal compared to demographic, genetic, and biomarker features, although it served as a useful replacement for biomarker panels when unavailable.
Publisher
bioRxiv
Published On
Apr 16, 2024
Authors
Oscar Aguilar, Cheng Chang, Elsa Bismuth, Manuel A Rivas
Tags
machine learning
multi-omics
disease risk prediction
UK Biobank
metabolomic data
biomarkers
survival models

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