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A Penalization Method for Estimating Heterogeneous Covariate Effects in Cancer Genomic Data

In high-throughput profiling studies, extensive efforts have been devoted to searching for the biomarkers associated with the development and progression of complex diseases. The heterogeneity of covariate effects associated with the outcomes across subjects has been noted in the literature. In this...

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Autores principales: Luo, Ziye, Zhang, Yuzhao, Sun, Yifan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9025588/
https://www.ncbi.nlm.nih.gov/pubmed/35456506
http://dx.doi.org/10.3390/genes13040702
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author Luo, Ziye
Zhang, Yuzhao
Sun, Yifan
author_facet Luo, Ziye
Zhang, Yuzhao
Sun, Yifan
author_sort Luo, Ziye
collection PubMed
description In high-throughput profiling studies, extensive efforts have been devoted to searching for the biomarkers associated with the development and progression of complex diseases. The heterogeneity of covariate effects associated with the outcomes across subjects has been noted in the literature. In this paper, we consider a scenario where the effects of covariates change smoothly across subjects, which are ordered by a known auxiliary variable. To this end, we develop a penalization-based approach, which applies a penalization technique to simultaneously select important covariates and estimate their unique effects on the outcome variables of each subject. We demonstrate that, under the appropriate conditions, our method shows selection and estimation consistency. Additional simulations demonstrate its superiority compared to several competing methods. Furthermore, applying the proposed approach to two The Cancer Genome Atlas datasets leads to better prediction performance and higher selection stability.
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spelling pubmed-90255882022-04-23 A Penalization Method for Estimating Heterogeneous Covariate Effects in Cancer Genomic Data Luo, Ziye Zhang, Yuzhao Sun, Yifan Genes (Basel) Article In high-throughput profiling studies, extensive efforts have been devoted to searching for the biomarkers associated with the development and progression of complex diseases. The heterogeneity of covariate effects associated with the outcomes across subjects has been noted in the literature. In this paper, we consider a scenario where the effects of covariates change smoothly across subjects, which are ordered by a known auxiliary variable. To this end, we develop a penalization-based approach, which applies a penalization technique to simultaneously select important covariates and estimate their unique effects on the outcome variables of each subject. We demonstrate that, under the appropriate conditions, our method shows selection and estimation consistency. Additional simulations demonstrate its superiority compared to several competing methods. Furthermore, applying the proposed approach to two The Cancer Genome Atlas datasets leads to better prediction performance and higher selection stability. MDPI 2022-04-15 /pmc/articles/PMC9025588/ /pubmed/35456506 http://dx.doi.org/10.3390/genes13040702 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Luo, Ziye
Zhang, Yuzhao
Sun, Yifan
A Penalization Method for Estimating Heterogeneous Covariate Effects in Cancer Genomic Data
title A Penalization Method for Estimating Heterogeneous Covariate Effects in Cancer Genomic Data
title_full A Penalization Method for Estimating Heterogeneous Covariate Effects in Cancer Genomic Data
title_fullStr A Penalization Method for Estimating Heterogeneous Covariate Effects in Cancer Genomic Data
title_full_unstemmed A Penalization Method for Estimating Heterogeneous Covariate Effects in Cancer Genomic Data
title_short A Penalization Method for Estimating Heterogeneous Covariate Effects in Cancer Genomic Data
title_sort penalization method for estimating heterogeneous covariate effects in cancer genomic data
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9025588/
https://www.ncbi.nlm.nih.gov/pubmed/35456506
http://dx.doi.org/10.3390/genes13040702
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