biostudies-literatureMusib LFundação para a Ciência e a Tecnologia45https://www.ebi.ac.uk/biostudies/studies/S-EPMC11523883biostudies-literatureUnknown17(1)<h4>Background</h4>High-dimensional omics data integration has emerged as a prominent avenue within the healthcare industry, presenting substantial potential to improve predictive models. However, the data integration process faces several challenges, including data heterogeneity, priority sequence in which data blocks are prioritized for rendering predictive information contained in multiple blocks, assessing the flow of information from one omics level to the other and multicollinearity.<h4>Methods</h4>We propose the Priority-Elastic net algorithm, a hierarchical regression method extending Priority-Lasso for the binary logistic regression model by incorporating a priority order for blocks of variables while fitting Elastic-net models sequentially for each block. The fitted values from eBioData miningPriority-Elastic net for binary disease outcome prediction based on multi-omics data.PMC11523883DOI: 10.54499/UIDB/00006/2020DOI: 10.54499/UIDB/00297/2020Lopes MBMourino HColetti RCarrasquinha EMusib LfalsePriority-Elastic net for binary disease outcome prediction based on multi-omics data.<h4>Background</h4>High-dimensional omics data integration has emerged as a prominent avenue within the healthcare industry, presenting substantial potential to improve predictive models. However, the data integration process faces several challenges, including data heterogeneity, priority sequence in which data blocks are prioritized for rendering predictive information contained in multiple blocks, assessing the flow of information from one omics level to the other and multicollinearity.<h4>Methods</h4>We propose the Priority-Elastic net algorithm, a hierarchical regression method extending Priority-Lasso for the binary logistic regression model by incorporating a priority order for blocks of variables while fitting Elastic-net models sequentially for each block. The fitted values from e2024-01-01T00:00:00Z2024 Oct2025-04-04T01:02:39.637Z2025-04-04T01:02:39.637ZS-EPMC115238833947294210.1186/s13040-024-00401-0