A computational system for Bayesian benchmark dose estimation of genomic data in BBMD.

ABSTRACT:

Background

Existing studies have revealed that the benchmark dose (BMD) estimates from short-term in vivo transcriptomics studies can approximate those from long-term guideline toxicity assessments. Existing software applications follow this trend by analyzing omics data through the maximum likelihood estimation and choosing the "best" model for BMD estimates. However, this practice ignores the model uncertainty and may result in over-confident inferences and predictions, leading to an inadequate decision.

Objective

By generally following the National Toxicology Program Approach to Genomic Dose-Response Modeling, we developed a web-based dose-response modeling and BMD estimation system, Bayesian BMD (BBMD), for genomic data to quantitatively address uncertainty from various

SUBMITTER: Ji C 

PROVIDER: S-EPMC8934139 | biostudies-literature | 2022 Mar

REPOSITORIES: biostudies-literature