ArticleIEEE control systems letters2023
Optimal Recursive Expert-Enabled Inference in Regulatory Networks.
Article in IEEE control systems letters, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Deep Reinforcement Learning for Intervention of Partially Observable Regulatory Networks.Proceedings of the ... American Control Conference. American Control Conference · 2025Article
- Pareto-Optimal Interventions in Gene Regulatory Networks using Signal Temporal Logic.Proceedings of the ... American Control Conference. American Control Conference · 2025Article
- Bayesian Topology Inference of Regulatory Networks under Partial Observability.Results in control and optimization · 2025Article
- Deep Reinforcement Learning Data Collection for Bayesian Inference of Hidden Markov Models.IEEE transactions on artificial intelligence · 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
- An optimal Bayesian intervention policy in response to unknown dynamic cell stimuli.Information sciences · 2024Article
- Bayesian reinforcement learning for navigation planning in unknown environments.Frontiers in artificial intelligence · 2024Article
- Reinforcement Learning Data-Acquiring for Causal Inference of Regulatory Networks.Proceedings of the ... American Control Conference. American Control ConferenceArticle
- Decentralized Reinforcement Learning for Asymmetric Gene Network Interventions.IEEE transactions on computational biology and bioinformaticsArticle
- Bayesian Lookahead Perturbation Policy for Inference of Regulatory Networks.IEEE/ACM 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
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Authors and funding
3 authors.
Funding
Abstract
Accurate inference of biological systems, such as gene regulatory networks and microbial communities, is a key to a deep understanding of their underlying mechanisms. Despite several advances in the inference of regulatory networks in recent years, the existing techniques cannot incorporate expert knowledge into the inference process. Expert knowledge contains valuable biological information and is often reflected in available biological data, such as interventions made by biologists for treating diseases. Given the complexity of regulatory networks and the limitation of biological data, ignoring expert knowledge can lead to inaccuracy in the inference process. This paper models the regulatory networks using Boolean network with perturbation. We develop an expert-enabled inference method for inferring the unknown parameters of the network model using expert-acquired data. Given the availability of information about data-acquiring objectives and expert confidence, the proposed method optimally quantifies the expert knowledge along with the temporal changes in the data for the inference process. The numerical experiments investigate the performance of the proposed method using the well-known p53-MDM2 gene regulatory network.
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