A Bayesian network approach for the identification of relationships between drivers of chlordecone bioaccumulation in plants

Abstract

Plants were sampled from four different types of chlordecone-contaminated land in Guadeloupe (West Indies). The objective was to investigate the importance of biological and agri-environmental parameters in the ability of plants to bioaccumulate chlordecone. Among the plant traits studied, only the growth habit significantly affected chlordecone transfer, since prostrate plants concentrated more chlordecone than erect plants. In addition, intensification of land use has led to a significant increase in the amount of chlordecone absorbed by plants. The use of Bayesian networks uncovers some hypothesis and identifies paths for reflection and possible studies to identify and quantify relationships that explain our data.

Simple Bayesian Gene Network Learning in Populus Drought Transcriptome Data

“…The core of these debates and controversies is the Markov condition/ assumption, an assumption made in the Bayesian probability theory. BN has been used to model chlordecone bioaccumulation in plants (Liber et al 2020) to discover the best regulators of drought response (Lahiri et al 2019) and to infer gene regulatory networks (Vignes et al 2011). A Bayesian gene network consists of a digraph, which connecting regulatory genes to their targets, and elegantly encodes conditional independence between genes.…”