权益分类	功能权益	普通用户	{{item.name}}会员
{{category.name}}	{{benefitItem.name}}

Learning and assessing the robustness of Bayesian networks for biological data

学习和评估生物数据贝叶斯网络的稳健性

基本信息

批准号：
2274458
负责人：
金额：
--
依托单位：
University of Leeds
依托单位国家：
英国
项目类别：
Studentship
财政年份：
2019
资助国家：
英国
起止时间：
2019 至无数据
项目状态：
已结题

来源：
https://gtr.ukri.org/projects?ref=studentship-2274458
关键词：
Learning assessing robustness Bayesian networks

项目摘要

Bayesian Network models are an increasingly popular way to analyse biological data. In Biology application, Bayesian Networks could be viewed as a machine which produces outputs from inputs. Here the inputs are data, hyper-parameters and distribution, and the outputs are biological meaningful results like the possibility of variable expression and its links to phenotype. While studying the construction of Bayesian Networks has led to many insights for target gene discovery and drug development, such models are typically computing-expensive. The computation cost caused three main problems in Bayesian Network's application to biological data. Firstly, the sensitivity of Bayesian Networks' outputs to the changes in inputs is rarely accessed. Secondly, biologists normally expect a faster analysis method, which limits the application of Bayesian Network to first-hand data. Last but not least, there is a gap in optimising such models' feature to achieve a more biologically meaningful result. Optimisation requires experiment repetition and design in the inputs space. Therefore, a faster method to achieve Bayesian Networks' results is demanded. We propose to develop a robust Bayesian emulator to mimic the result from the Bayesian Network model efficiently, which could reduce the computation cost of Bayesian Networks. We will also explore novel biological references to validate the credibility of our model. Thus we could do experiment design over different inputs and optimise the performance of Bayesian Networks. The improved method would have potential in general biology analysis practice.

贝叶斯网络模型是分析生物数据的一种越来越流行的方法。在生物学应用中，贝叶斯网络可以被看作是一台从输入产生输出的机器。在这里，输入是数据、超参数和分布，输出是有生物学意义的结果，如变量表达的可能性及其与表型的联系。虽然研究贝叶斯网络的构建已经为靶基因发现和药物开发带来了许多见解，但这种模型通常计算昂贵。计算成本导致贝叶斯网络应用于生物数据的三个主要问题。首先，贝叶斯网络的输出对输入变化的敏感性很少被访问。其次，生物学家通常期望更快的分析方法，这限制了贝叶斯网络在第一手数据中的应用。最后但并非最不重要的是，在优化这些模型的特征以实现更具生物学意义的结果方面存在差距。优化需要在输入空间中进行实验重复和设计。因此，需要一种更快的方法来实现贝叶斯网络的结果。我们建议开发一个健壮的贝叶斯仿真器来有效地模拟贝叶斯网络模型的结果，这可以减少贝叶斯网络的计算成本。我们还将探索新的生物参考，以验证我们的模型的可信度。因此，我们可以在不同的输入进行实验设计，优化贝叶斯网络的性能。改进后的方法在一般生物分析实践中具有潜力。