Modelling survival functions and their critical points

生存函数及其临界点建模

• 批准号: 2278010
• 金额: --
• 依托单位: University of Edinburgh
• 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: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2278010
• 关键词: Modelling; survival; functions; their; critical

The main part of this research addresses the problem of modelling a periodic interaction between two biological variables. We model the interaction of the two variables by a centred two-dimensional Gaussian process with Markovian structure, as suggested by Bart et al. (2005). We develop appropriate confidence regions for estimating periodicity and the strength of the interaction and we adapt the model to deal with data with missing observations. We also discuss how identifiability of the periodicity is related to the experimental design. We apply this model to identify pairs of genes in yeast that interact periodically. The approach is then applied to model periodicity in the disease progression of acute pancre- atitis (AP), a frequent and potentially fatal systemic inflammatory disease, where the disease progression is described by two opposing factors, comprising of putative 'protective' and 'damaging' variables. In particular, we show that the first two principal components in the AP data can be interpreted as the 'protective' and 'damaging' variables and have a clear clinical interpretation, estimate periodicity of the disease progression for observed data with missing observations and rationalise the timing and opportunities for optimal medical intervention. We then reformulate this model into a latent growth model, which can more easily handle non- regularly spaced observations and missing data, enabling the use of a wider range of data sets. The remaining part of the research involves developing inference for exponentially decaying periodic functions, in particular when there is asymmetry in the decay rates of the maxima and minima of the periodic function. The inference is developed on simulated data and then applied to circadian gene expression data obtained from murine brain slices.

本研究的主要部分解决了两个生物变量之间的周期性相互作用建模的问题。我们通过具有马尔可夫结构的中心二维高斯过程来模拟两个变量的相互作用，正如Bart等人（2005）所建议的那样。我们开发适当的置信区域估计周期性和相互作用的强度，我们适应的模型来处理缺失观测数据。我们还讨论了如何识别的周期性与实验设计。我们应用这个模型来识别酵母中周期性相互作用的基因对。然后，该方法被应用于急性胰腺炎（AP），一种常见的和潜在的致命的全身性炎症性疾病的疾病进展的周期性模型，其中疾病进展是由两个相反的因素，包括推定的“保护”和“破坏”变量描述。特别是，我们表明，前两个主成分的AP数据可以被解释为“保护”和“破坏”的变量，并有一个明确的临床解释，估计周期性的疾病进展的观察数据与缺失的观察和合理化的时间和机会，最佳的医疗干预。然后，我们将此模型重新表述为潜在增长模型，该模型可以更容易地处理非规则间隔的观测和缺失数据，从而能够使用更广泛的数据集。研究的其余部分涉及发展指数衰减周期函数的推断，特别是当周期函数的最大值和最小值的衰减率不对称时。推理是建立在模拟数据上的，然后应用于从小鼠脑切片获得的昼夜节律基因表达数据。