DAG-VAERL: a novel causal inference method for building causal gene regulatory networks

BACKGROUND: Causal discovery methods provide a powerful tool for uncovering the causal relationships between lncRNAs and target genes in gene regulations. In many causal inference and structure learning tasks, learning the Directed Acyclic Graph (DAG) structure from data is a challenging problem. Traditional DAG learning methods often rely on heuristic searches or strict constraints, which fail to effectively handle complex nonlinear relationships and discrete data. METHODS: To address this, we propose a novel deep generative model-DAG-VAERL, which combines Graph Neural Networks (GNN) as well as Reinforcement Learning (RL) frameworks and Graph Attention Networks (GAT) module, leveraging Variational Autoencoders (VAE) to learn the DAG structure. DAG-VAERL is capable of modeling complex dependencies between nodes through GNNs and optimizing the graph structure using RL strategies. RESULTS: We conduct extensive experiments on synthetic and real-world datasets, including Alzheimer's disease data, to validate the superiority of DAG-VAERL in structural discovery and parameter estimation. Experimental results demonstrate that DAG-VAERL significantly outperforms traditional methods in structure recovery, especially when dealing with complex data involving nonlinear and discrete variables. CONCLUSIONS: This model not only effectively learns the DAG structure from data but also serves as a powerful tool for causal inference and other graph-bard analysis tasks, providing a new approach for related fields.