...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>IEEE/ACM transactions on computational biology and bioinformatics >Batch Mode TD($lambda$ ) for Controlling Partially Observable Gene Regulatory Networks
【24h】

Batch Mode TD($lambda$ ) for Controlling Partially Observable Gene Regulatory Networks

机译：批处理模式TD（$ lambda $），用于控制部分可观察到的基因调控网络

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

External control of gene regulatory networks (GRNs) has received much attention in recent years. The aim is to find a series of actions to apply to a gene regulation system making it avoid its diseased states. In this work, we propose a novel method for controlling partially observable GRNs combining batch mode reinforcement learning (Batch RL) and TD(λ) algorithms. Unlike the existing studies inferring a computational model from gene expression data, and obtaining a control policy over the constructed model, our idea is to interpret the time series gene expression data as a sequence of observations that the system produced, and obtain an approximate stochastic policy directly from the gene expression data without estimation of the internal states of the partially observable environment. Thereby, we get rid of the most time consuming phases of the existing studies, inferring a model and running the model for the control. Results show that our method is able to provide control solutions for regulation systems of several thousands of genes only in seconds, whereas existing studies cannot solve control problems of even a few dozens of genes. Results also show that our approximate stochastic policies are almost as good as the policies generated by the existing studies.
机译：近年来，基因调控网络（GRN）的外部控制已引起广泛关注。目的是找到一系列适用于基因调节系统的作用，使其避免患病。在这项工作中，我们提出了一种结合批处理模式强化学习（Batch RL）和TD（λ）算法来控制部分可观察GRN的新颖方法。与现有研究从基因表达数据推断计算模型并获得对构建模型的控制策略不同，我们的想法是将时间序列基因表达数据解释为系统产生的一系列观测结果，并获得近似的随机策略。直接从基因表达数据中获得，而无需估计部分可观察环境的内部状态。从而，我们摆脱了现有研究中最耗时的阶段，推断出模型并运行模型进行控制。结果表明，我们的方法仅能在几秒钟内为数千个基因的调控系统提供控制解决方案，而现有的研究甚至无法解决数十个基因的控制问题。结果还表明，我们的近似随机策略几乎与现有研究生成的策略一样好。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号