Dataset Information

Bayesian Pathway Analysis for Complex Interactions.

ABSTRACT: Modern epidemiologic studies permit investigation of the complex pathways that mediate effects of social, behavioral, and molecular factors on health outcomes. Conventional analytical approaches struggle with high-dimensional data, leading to high likelihoods of both false-positive and false-negative inferences. Herein, we describe a novel Bayesian pathway analysis approach, the algorithm for learning pathway structure (ALPS), which addresses key limitations in existing approaches to complex data analysis. ALPS uses prior information about pathways in concert with empirical data to identify and quantify complex interactions within networks of factors that mediate an association between an exposure and an outcome. We illustrate ALPS through application to a complex gene-drug interaction analysis in the Predictors of Breast Cancer Recurrence (ProBe CaRe) Study, a Danish cohort study of premenopausal breast cancer patients (2002-2011), for which conventional analyses severely limit the quality of inference.

SUBMITTER: Baurley JW

PROVIDER: S-EPMC7822643 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Bayesian Pathway Analysis for Complex Interactions.

Baurley James W JW Kjærsgaard Anders A Zwick Michael E ME Cronin-Fenton Deirdre P DP Collin Lindsay J LJ Damkier Per P Hamilton-Dutoit Stephen S Lash Timothy L TL Ahern Thomas P TP

American journal of epidemiology 20201201 12

Modern epidemiologic studies permit investigation of the complex pathways that mediate effects of social, behavioral, and molecular factors on health outcomes. Conventional analytical approaches struggle with high-dimensional data, leading to high likelihoods of both false-positive and false-negative inferences. Herein, we describe a novel Bayesian pathway analysis approach, the algorithm for learning pathway structure (ALPS), which addresses key limitations in existing approaches to complex dat ...[more]

PMID: 32639515

Similar Datasets

Project description:BACKGROUND: microRNAs (miRNAs) regulate target gene expression by controlling their mRNAs post-transcriptionally. Increasing evidence demonstrates that miRNAs play important roles in various biological processes. However, the functions and precise regulatory mechanisms of most miRNAs remain elusive. Current research suggests that miRNA regulatory modules are complicated, including up-, down-, and mix-regulation for different physiological conditions. Previous computational approaches for discovering miRNA-mRNA interactions focus only on down-regulatory modules. In this work, we present a method to capture complex miRNA-mRNA interactions including all regulatory types between miRNAs and mRNAs. RESULTS: We present a method to capture complex miRNA-mRNA interactions using Bayesian network structure learning with splitting-averaging strategy. It is designed to explore all possible miRNA-mRNA interactions by integrating miRNA-targeting information, expression profiles of miRNAs and mRNAs, and sample categories. We also present an analysis of data sets for epithelial and mesenchymal transition (EMT). Our results show that the proposed method identified all possible types of miRNA-mRNA interactions from the data. Many interactions are of tremendous biological significance. Some discoveries have been validated by previous research, for example, the miR-200 family negatively regulates ZEB1 and ZEB2 for EMT. Some are consistent with the literature, such as LOX has wide interactions with the miR-200 family members for EMT. Furthermore, many novel interactions are statistically significant and worthy of validation in the near future. CONCLUSIONS: This paper presents a new method to explore the complex miRNA-mRNA interactions for different physiological conditions using Bayesian network structure learning with splitting-averaging strategy. The method makes use of heterogeneous data including miRNA-targeting information, expression profiles of miRNAs and mRNAs, and sample categories. Results on EMT data sets show that the proposed method uncovers many known miRNA targets as well as new potentially promising miRNA-mRNA interactions. These interactions could not be achieved by the normal Bayesian network structure learning.

Dataset Information

Bayesian Pathway Analysis for Complex Interactions.

Publications

Bayesian Pathway Analysis for Complex Interactions.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets