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In addition to interactive data analyses and visualisations, DTB allows data exports and downloads via options in the side panel, Information tab (Fig. 3). If the Search tab is selected in the side panel, options for annotation of disease nodes and transition edges are available via the drop-down menus. Node annotation offers four options (ICD-10 code and description, ICD-10 code, Diagnosis code description and None), and Edge annotation offers three options (Number of patients, Relative risk, and No annotation; Fig. 5). Nodes remain constant irrespective of selection, whereas the edge will change thickness and shade of grey once an annotation is selected. The higher the annotation value (number of patients or relative risk) the thicker and darker the edge will be. Further exploration of disease trajectories is achieved by clicking on each transition edge in the network and selecting the Information tab in the browser side panel. The user will find the relative risk, P-values, number of patients, time between diagnoses, mean age at the trajectory inception, and 5-year mortality count at trajectory end. Moreover, users can also search for diagnosis pairs at Google Scholar via a direct link and choose to delete edges or nodes (Fig. 3).
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To create an overview of the raw summary statistics data, data can be summarised in a tabular format by clicking View data table in the side panel in the Information tab. All trajectories made with DTB can be downloaded in several formats for further data processing, analyses, and presentation of search results. Cytoscape JSON format exports are available for downloading43. The Cytoscape files can be opened locally in the Cytoscape desktop application for rearranging or modifying the visualisation further. Export of search results as linear disease trajectories and disease trajectory networks is also possible in PNG format (with transparent background) or JPEG with white background. The raw comma-separated data values (CSV) are available for download for easy data import into Excel or data science programming applications. 041b061a72