HSM: Construction of graph-based network longitudinal algorithms to identify screening and prognostic biomarkers and therapeutic targets (GBNLA)

在系统医学中开发高度要求的个性化方法遇到了这样一种情况，即大数据的数量显著影响了用于分析它们的数据分析方法。典型的大数据包含高维数据，包括可以是连续或分类的参数。此外，该数据可以来自同一患者随时间进行的连续测量。因此，需要开发能够分析包含分类和纵向连续数据的高维数据中的不同变化的方法，以便鉴定预后、诊断和治疗靶点，例如，以解决患病/健康患者的分类任务，特别是用于早期诊断。本申请的主要目的是开发一种方法，用于以网络的形式表示包含分类和连续参数的序列数据，对网络动态进行纵向分析，从而构建纵向网络生物标志物，生成诊断，预后和药物靶点。如果我们利用亲合网络分析的思想，这就成为可能。parenclitic网络分析的主要优点是，它使一个图形的建设没有任何先验知识的参数之间的相互作用。一种构建parenclitic网络的算法，能够在参数/节点之间建立链接，而无需任何关于它们相互作用的先验知识，最早由Zanin和Bocaletti描述。Parenclitic网络已成功应用于不同疾病中关键基因和代谢产物的检测。最近，我们应用这种方法来实现对携带癌症发展特征的人类DNA甲基化数据的机器学习分类。我们还描述了一种改进的算法来构建parenclitic网络，并提供了一个简单的案例研究的蛋白质数据集从卵巢癌病例和对照血清样本。在本项目中，我们计划开发和研究这种算法，以连接网络的形式表示连续和分类的高维数据，而无需分析物-分析物链接的先验知识，并使用这种表示方法通过构建纵向网络生物标志物模型来搜索诊断和预后标记物以及可药物化的目标。计划的研究将包括结合parenclitic网络分析与我们以前开发的算法进行串行数据分析。串行数据分析已经显示出其优于典型快照分析的优势，能够检测数据中的时间相关变化，从而与非串行数据相比能够更早地进行诊断。方法学开发将包括使用不同的可用临床和流行病学数据（如蛋白质组学、遗传学和DNA甲基化数据）测试算法，以确定研究方法的数据特定特征。此外，我们的网络分析将与其他最近开发的网络分析方法相结合，包括社区检测和深度学习算法。为了研究所开发的方法的优点和缺点，并精确地估计其效率，我们将生成密切模仿癌症筛查数据的合成数据。我们不仅将研究我们的方法在疾病早期诊断中的应用，还将研究检测到的疾病的个性化区分。这项工作将与其他致力于网络机器学习算法开发的科学家以及临床医生进行沟通，以讨论不同的临床数据以及我们的方法在临床实践中的实施。

Development of heavily requested personalised approaches in Systems Medicine have encountered a situation in which the amount of Big Data significantly overwhelms the data analysis methods used to analyse them. Typical Big Data contains high-dimensional data including parameters which can be continuous or categorical. Additionally, this data can be from serial measurements taken over time from the same patients. Hence, there is a need to develop methodology able to analyse different changes in high-dimensional data containing categorical and longitudinal continuous data in order to identify prognostic, diagnostic and therapeutic targets, e.g., to solve the task of classification of diseased/healthy patients, particularly for early diagnosis. The main aim of this application is to develop a methodology for representation of serial data containing categorical and continuous parameters in the form of networks, the longitudinal analysis of network dynamics, and hence, the construction of longitudinal network biomarkers, generating diagnostic, prognostic and druggable targets. This becomes possible if we utilise the idea of parenclitic network analysis. The main advantage of parenclitic network analysis is that it enables the construction of a graph without any a priori knowledge of the interactions between the parameters. An algorithm to build parenclitic networks, able to establish links between parameters/nodes without any a priori knowledge of their interactions was first described by Zanin and Bocaletti. Parenclitic networks have been successfully applied to the problem of detecting key genes and metabolites in different diseases. Recently we have applied this methodology to implement a machine learning classification of human DNA methylation data carrying signatures of cancer development. We have also described an improved algorithm to construct parenclitic networks and provided a simple case study of a protein dataset from ovarian cancer case and control serum samples. In the present project we plan to develop and investigate this algorithm to represent serial high-dimensional data, both continuous and categorical, in the form of connected networks without a priori knowledge of analyte-analyte links and to use this representation to search for diagnostic and prognostic markers and druggable targets via the construction of longitudinal network biomarker models. The research planned will include the combination of parenclitic network analysis with our previously developed algorithms for serial data analysis. Serial data analysis has shown its advantage over the typical snapshot analysis, being able to detect time-dependent changes in the data to enable earlier diagnosis in comparison to non-serial data. Methodological development will include testing of algorithms with different available clinical and epidemiological data, such as proteomic, genetic, and DNA methylation data, in order to identify data-specific features of the methodology under investigation. Moreover, our network analysis will be combined with other recently developed network analysis methods, including community detection and deep learning algorithms. In order to investigate the advantages and disadvantages of the developed methodology and precisely estimate its efficiency, we will generate synthetic data which closely mimic cancer screening data. We will investigate not only the application of our methodology to the early diagnosis of diseases, but also to the personalised differentiation of the diseases detected. The work will be communicated with other scientists contributing to the development of network machine learning algorithms as well as clinicians to discuss different clinical data and the implementation of our methodology in clinical practice.