Graph-guided Bayesian Factor Model for Integrative Analysis of Multi-modal Data with Noisy Network Information.

ABSTRACT: There is a growing body of literature on factor analysis that can capture individual and shared structures in multi-modal data. However, few of these approaches incorporate biological knowledge such as functional genomics and functional metabolomics. Graph-guided statistical learning methods that can incorporate knowledge of underlying networks have been shown to improve predication and classification accuracy, and yield more interpretable results. Moreover, these methods typically use graphs extracted from existing databases or rely on subject matter expertise which are known to be incomplete and may contain false edges. To address this gap, we propose a graph-guided Bayesian factor model that can account for network noise and identify globally shared, partially shared and modality-specif

SUBMITTER: Li W 

PROVIDER: S-EPMC12221265 | biostudies-literature | 2024 Aug

REPOSITORIES: biostudies-literature