Priority-Elastic net for binary disease outcome prediction based on multi-omics data

Bibliographic Details
Main Author: Musib, Laila
Publication Date: 2024
Other Authors: Coletti, Roberta, Lopes, Marta B., Mouriño, Helena, Carrasquinha, Eunice
Format: Article
Language: eng
Source: Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
Download full: http://hdl.handle.net/10362/178923
Summary: Publisher Copyright: © The Author(s) 2024.
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spelling Priority-Elastic net for binary disease outcome prediction based on multi-omics dataAdaptive-Elastic netElastic-netHigh-dimensional dataLogistic regressionMulti-omics dataPriority-LassoBiochemistryMolecular BiologyGeneticsComputer Science ApplicationsComputational Theory and MathematicsComputational MathematicsSDG 3 - Good Health and Well-beingPublisher Copyright: © The Author(s) 2024.Background: 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. Methods: 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 each step are then used as an offset in the subsequent step. Additionally, we considered the adaptive elastic-net penalty within our priority framework to compare the results. Results: The Priority-Elastic net and Priority-Adaptive Elastic net algorithms were evaluated on a brain tumor dataset available from The Cancer Genome Atlas (TCGA), accounting for transcriptomics, proteomics, and clinical information measured over two glioma types: Lower-grade glioma (LGG) and glioblastoma (GBM). Conclusion: Our findings suggest that the Priority-Elastic net is a highly advantageous choice for a wide range of applications. It offers moderate computational complexity, flexibility in integrating prior knowledge while introducing a hierarchical modeling perspective, and, importantly, improved stability and accuracy in predictions, making it superior to the other methods discussed. This evolution marks a significant step forward in predictive modeling, offering a sophisticated tool for navigating the complexities of multi-omics datasets in pursuit of precision medicine’s ultimate goal: personalized treatment optimization based on a comprehensive array of patient-specific data. This framework can be generalized to time-to-event, Cox proportional hazards regression and multicategorical outcomes. A practical implementation of this method is available upon request in R script, complete with an example to facilitate its application.Faculdade de Ciências e Tecnologia (FCT)CMA - Centro de Matemática e AplicaçõesUNIDEMI - Unidade de Investigação e Desenvolvimento em Engenharia Mecânica e IndustrialRUNMusib, LailaColetti, RobertaLopes, Marta B.Mouriño, HelenaCarrasquinha, Eunice2025-02-12T21:19:56Z2024-122024-12-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10362/178923eng1756-0381PURE: 107717659https://doi.org/10.1186/s13040-024-00401-0info:eu-repo/semantics/openAccessreponame:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiainstacron:RCAAP2025-02-24T01:44:15Zoai:run.unl.pt:10362/178923Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireinfo@rcaap.ptopendoar:https://opendoar.ac.uk/repository/71602025-05-28T20:39:37.663506Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) - FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiafalse
dc.title.none.fl_str_mv Priority-Elastic net for binary disease outcome prediction based on multi-omics data
title Priority-Elastic net for binary disease outcome prediction based on multi-omics data
spellingShingle Priority-Elastic net for binary disease outcome prediction based on multi-omics data
Musib, Laila
Adaptive-Elastic net
Elastic-net
High-dimensional data
Logistic regression
Multi-omics data
Priority-Lasso
Biochemistry
Molecular Biology
Genetics
Computer Science Applications
Computational Theory and Mathematics
Computational Mathematics
SDG 3 - Good Health and Well-being
title_short Priority-Elastic net for binary disease outcome prediction based on multi-omics data
title_full Priority-Elastic net for binary disease outcome prediction based on multi-omics data
title_fullStr Priority-Elastic net for binary disease outcome prediction based on multi-omics data
title_full_unstemmed Priority-Elastic net for binary disease outcome prediction based on multi-omics data
title_sort Priority-Elastic net for binary disease outcome prediction based on multi-omics data
author Musib, Laila
author_facet Musib, Laila
Coletti, Roberta
Lopes, Marta B.
Mouriño, Helena
Carrasquinha, Eunice
author_role author
author2 Coletti, Roberta
Lopes, Marta B.
Mouriño, Helena
Carrasquinha, Eunice
author2_role author
author
author
author
dc.contributor.none.fl_str_mv Faculdade de Ciências e Tecnologia (FCT)
CMA - Centro de Matemática e Aplicações
UNIDEMI - Unidade de Investigação e Desenvolvimento em Engenharia Mecânica e Industrial
RUN
dc.contributor.author.fl_str_mv Musib, Laila
Coletti, Roberta
Lopes, Marta B.
Mouriño, Helena
Carrasquinha, Eunice
dc.subject.por.fl_str_mv Adaptive-Elastic net
Elastic-net
High-dimensional data
Logistic regression
Multi-omics data
Priority-Lasso
Biochemistry
Molecular Biology
Genetics
Computer Science Applications
Computational Theory and Mathematics
Computational Mathematics
SDG 3 - Good Health and Well-being
topic Adaptive-Elastic net
Elastic-net
High-dimensional data
Logistic regression
Multi-omics data
Priority-Lasso
Biochemistry
Molecular Biology
Genetics
Computer Science Applications
Computational Theory and Mathematics
Computational Mathematics
SDG 3 - Good Health and Well-being
description Publisher Copyright: © The Author(s) 2024.
publishDate 2024
dc.date.none.fl_str_mv 2024-12
2024-12-01T00:00:00Z
2025-02-12T21:19:56Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10362/178923
url http://hdl.handle.net/10362/178923
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 1756-0381
PURE: 107717659
https://doi.org/10.1186/s13040-024-00401-0
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
instacron:RCAAP
instname_str FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
instacron_str RCAAP
institution RCAAP
reponame_str Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
collection Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
repository.name.fl_str_mv Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) - FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
repository.mail.fl_str_mv info@rcaap.pt
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