ArticleIEEE/ACM transactions on computational biology and bioinformatics
Modeling Defensive Response of Cells to Therapies: Equilibrium Interventions for Regulatory Networks.
Article in IEEE/ACM transactions on computational biology and bioinformatics. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
7 citing papers in PubMed, 7 citations in OpenAlex.
- 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
- Bayesian Optimization for State and Parameter Estimation of Dynamic Networks with Binary Space.Control Technology and Applications. Control Technology and Applications · 2024Article
- Dynamic Intervention in Gene Regulatory Networks: A Partially Observed Zero-Sum Markov Game.Control Technology and Applications. Control Technology and Applications · 2024Article
- Decentralized Reinforcement Learning for Asymmetric Gene Network Interventions.IEEE transactions on computational biology and bioinformaticsArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors at 1 institution in 1 country.
Funding
Abstract
A major objective in genomics is to design interventions that can shift undesirable behaviors of such systems (i.e., those associated with cancers) into desirable ones. Several intervention policies have been developed in recent years, including dynamic and structural interventions. These techniques aim at making targeted changes to cell dynamics upon intervention, without considering the cell's defensive mechanisms to interventions. This simplified assumption often leads to early and short-term success of interventions, followed by partial or full recurrence of diseases. This is due to the fact that cells often have dynamic and intelligent responses to interventions through internal stimuli. This paper models gene regulatory networks (GRNs) using the Boolean network with perturbation. The dynamic and adaptive battle between intervention and the cell is modeled as a two-player zero-sum game, where intervention and the cell fight against each other with fully opposite objectives. An optimal intervention policy is obtained as a Nash equilibrium solution, through which the intervention is stochastic, ensuring the optimal solution to all potential cell responses. We analytically analyze the superiority of the proposed intervention policy against existing intervention techniques. Comprehensive numerical experiments using the p53-MDM2 negative feedback loop regulatory network and melanoma network demonstrate the high performance of the proposed method.
Indexed as
Identifiers
What OpenQuestion holds
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.