ArticleFrontiers in control engineering2022
Inference of regulatory networks through temporally sparse data.
Article in Frontiers in control engineering, 2022. 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, 18 citations in OpenAlex.
- Temporal network analysis in systems biology: concepts, inference, and validation.Frontiers in bioinformatics · 2026Review
- Bayesian Topology Inference of Regulatory Networks under Partial Observability.Results in control and optimization · 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
- Kernel-Based Particle Filtering for Scalable Inference in Partially Observed Boolean Dynamical Systems.IFAC-PapersOnLine · 2024Article
- Deep Reinforcement Learning Sensor Scheduling for Effective Monitoring of Dynamical Systems.Systems science & control engineering · 2024Article
- Reinforcement Learning Data-Acquiring for Causal Inference of Regulatory Networks.Proceedings of the ... American Control Conference. American Control ConferenceArticle
- Bayesian Lookahead Perturbation Policy for Inference of Regulatory Networks.IEEE/ACM transactions on computational biology and bioinformaticsArticle
- 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 at 1 institution in 1 country.
Funding
Abstract
A major goal in genomics is to properly capture the complex dynamical behaviors of gene regulatory networks (GRNs). This includes inferring the complex interactions between genes, which can be used for a wide range of genomics analyses, including diagnosis or prognosis of diseases and finding effective treatments for chronic diseases such as cancer. Boolean networks have emerged as a successful class of models for capturing the behavior of GRNs. In most practical settings, inference of GRNs should be achieved through limited and temporally sparse genomics data. A large number of genes in GRNs leads to a large possible topology candidate space, which often cannot be exhaustively searched due to the limitation in computational resources. This paper develops a scalable and efficient topology inference for GRNs using Bayesian optimization and kernel-based methods. Rather than an exhaustive search over possible topologies, the proposed method constructs a Gaussian Process (GP) with a topology-inspired kernel function to account for correlation in the likelihood function. Then, using the posterior distribution of the GP model, the Bayesian optimization efficiently searches for the topology with the highest likelihood value by optimally balancing between exploration and exploitation. The performance of the proposed method is demonstrated through comprehensive numerical experiments using a well-known mammalian cell-cycle 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.