{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Baena-Miret S"],"funding":["Ministerio de Ciencia e Innovación"],"pagination":["e0307482"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC11265675"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["19(7)"],"pubmed_abstract":["High-throughput technologies have generated vast amounts of omic data. It is a consensus that the integration of diverse omics sources improves predictive models and biomarker discovery. However, managing multiple omics data poses challenges such as data heterogeneity, noise, high-dimensionality and missing data, especially in block-wise patterns. This study addresses the challenges of high dimensionality and block-wise missing data through a regularization and constrained-based approach. The methodology is implemented in the R package bwm for binary and continuous response variables, and applied to breast cancer and exposome multi-omics datasets, achieving strong performance even in scenarios with missing data present in all omics. In binary classification task, our proposed model achieve"],"journal":["PloS one"],"pubmed_title":["A framework for block-wise missing data in multi-omics."],"pmcid":["PMC11265675"],"funding_grant_id":["PID2019- 104830RB-I00/AEI DOI: 10.13039/501100011033"],"pubmed_authors":["Baena-Miret S","Vegas E","Reverter F"],"additional_accession":[]},"is_claimable":false,"name":"A framework for block-wise missing data in multi-omics.","description":"High-throughput technologies have generated vast amounts of omic data. It is a consensus that the integration of diverse omics sources improves predictive models and biomarker discovery. However, managing multiple omics data poses challenges such as data heterogeneity, noise, high-dimensionality and missing data, especially in block-wise patterns. This study addresses the challenges of high dimensionality and block-wise missing data through a regularization and constrained-based approach. The methodology is implemented in the R package bwm for binary and continuous response variables, and applied to breast cancer and exposome multi-omics datasets, achieving strong performance even in scenarios with missing data present in all omics. In binary classification task, our proposed model achieve","dates":{"release":"2024-01-01T00:00:00Z","publication":"2024","modification":"2026-06-01T10:01:25.565Z","creation":"2025-04-19T13:13:07.77Z"},"accession":"S-EPMC11265675","cross_references":{"pubmed":["39042603"],"doi":["10.1371/journal.pone.0307482"]}}