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

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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.