A hierarchical constrained density regression model for predicting cluster-level dose-response.

ABSTRACT: With the advent of new alternative methods for rapid toxicity screening of chemicals comes the need for new statistical methodologies which appropriately synthesize the large amount of data collected. For example, transcriptomic assays can be used to assess the impact of a chemical on thousands of genes, but current approaches to analyzing the data treat each gene separately and do not allow sharing of information among genes within pathways. Furthermore, the methods employed are fully parametric and do not account for changes in distribution shape that may occur at high exposure levels. To address the limitations of these methods, we propose Constrained Logistic Density Regression (COLDER) to model expression data from different genes simultaneously. Under COLDER, the dose-response functi

SUBMITTER: Pennell ML 

PROVIDER: S-EPMC12396741 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature