ArticleProceedings of the ... American Control Conference. American Control Conference
Reinforcement Learning Data-Acquiring for Causal Inference of Regulatory Networks.
Article in Proceedings of the ... American Control Conference. American Control Conference. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Bayesian Topology Inference of Regulatory Networks under Partial Observability.Results in control and optimization · 2025Article
- MulNet: a scalable framework for reconstructing intra- and intercellular signaling networks from bulk and single-cell RNA-seq data.Briefings in bioinformatics · 2025Article
- Dynamic Intervention in Gene Regulatory Networks: A Partially Observed Zero-Sum Markov Game.Control Technology and Applications. Control Technology and Applications · 2024Article
- Bayesian Optimization for State and Parameter Estimation of Dynamic Networks with Binary Space.Control Technology and Applications. Control Technology and Applications · 2024Article
- Optimal Inference of Hidden Markov Models Through Expert-Acquired Data.IEEE transactions on artificial intelligence · 2024Article
- An optimal Bayesian intervention policy in response to unknown dynamic cell stimuli.Information sciences · 2024Article
- Causal Inference Meets Deep Learning: A Comprehensive Survey.Research (Washington, D.C.) · 2024Review
- Deep Reinforcement Learning Sensor Scheduling for Effective Monitoring of Dynamical Systems.Systems science & control engineering · 2024Article
- Bayesian reinforcement learning for navigation planning in unknown environments.Frontiers in artificial intelligence · 2024Article
- Decentralized Reinforcement Learning for Asymmetric Gene Network Interventions.IEEE transactions on computational biology and bioinformaticsArticle
- Modeling Defensive Response of Cells to Therapies: Equilibrium Interventions for Regulatory Networks.IEEE/ACM transactions on computational biology and bioinformaticsArticle
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
Gene regulatory networks (GRNs) consist of multiple interacting genes whose activities govern various cellular processes. The limitations in genomics data and the complexity of the interactions between components often pose huge uncertainties in the models of these biological systems. Meanwhile, inferring/estimating the interactions between components of the GRNs using data acquired from the normal condition of these biological systems is a challenging or, in some cases, an impossible task. Perturbation is a well-known genomics approach that aims to excite targeted components to gather useful data from these systems. This paper models GRNs using the Boolean network with perturbation, where the network uncertainty appears in terms of unknown interactions between genes. Unlike the existing heuristics and greedy data-acquiring methods, this paper provides an optimal Bayesian formulation of the data-acquiring process in the reinforcement learning context, where the actions are perturbations, and the reward measures step-wise improvement in the inference accuracy. We develop a semi-gradient reinforcement learning method with function approximation for learning near-optimal data-acquiring policy. The obtained policy yields near-exact Bayesian optimality with respect to the entire uncertainty in the regulatory network model, and allows learning the policy offline through planning. We demonstrate the performance of the proposed framework using the well-known p53-Mdm2 negative feedback loop gene regulatory network.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.