GRASP: a Bayesian network structure learning method using adaptive sequential Monte Carlo

Abstract

Bayesian networks (BNs) provide a probabilistic, graphical framework for modeling highdimensional joint distributions with complex dependence structures. BNs can be used to infer complex biological networks using heterogeneous data from different sources with missing values. Despite extensive studies in the past, network structure learning from data is still a challenging open question in BN research. In this study, we present a sequential Monte Carlo (SMC) based three-stage approach, GRowth-based Approach with Staged Pruning (GRASP). A double filtering strategy was first used for discovering the overall skeleton of the target BN. To search for the optimal network structures we designed an adaptive SMC (adSMC) algorithm to increase the diversity of sampled networks which were further improved by a new stage to reclaim edges missed in the skeleton discovery step. GRASP gave very satisfactory results when tested on benchmark networks. Finally, BN structure learning using multiple types of genomics data illustrates GRASP's potential in discovering novel biological relationships in integrative genomic studies.

Genetic factors associated with cancer racial disparity – an integrative study across twenty‐one cancer types

“…We used a biological network inference method, GRASP, which we recently developed [44] to build a Bayesian network (BN) model for selected genomic features using several types of genomic data available from TCGA, including mRNA‐seq, microRNA‐seq, protein expression, and DNA methylation data. We first used RNA‐seq data to identify transcripts highly correlated with a feature of interest (XKR9 gene expression in this study).…”