ArticleStatistics in medicine2023
Identifying important gene signatures of BMI using network structure-aided nonparametric quantile regression.
Article in Statistics in medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Low-Rank Variational Correction Estimation for Multi-Source Heterogeneous Quantile Linear Regression Models.Statistics in medicine · 2026Article
- Bayesian fractional polynomial approach to quantile regression and variable selection with application in the analysis of blood pressure among US adults.Journal of applied statistics · 2025Article
- Information-incorporated sparse convex clustering for disease subtyping.Bioinformatics (Oxford, England) · 2023Article
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Authors and funding
3 authors.
Funding
Abstract
We focus on identifying genomics risk factors of higher body mass index (BMI) incorporating a priori information, such as biological pathways. However, the commonly used methods to incorporate prior information provide a model for the mean function of the outcome and rely on unmet assumptions. To address these concerns, we propose a method for nonparametric additive quantile regression with network regularization to incorporate the information encoded by known networks. To account for nonlinear associations, we approximate the unknown additive functional effect of each predictor with the expansion of a B-spline basis. We implement the group Lasso penalty to obtain a sparse model. We define the network-constrained penalty by the total
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