A visual analysis approach and the developed supporting technology provide a comprehensive solution for analyzing large and complex integrated genomic and biomedical data. This paper presents a methodology that is implemented as an interactive visual analysis technology for extracting knowledge from complex genetic and clinical data and then visualizing it in a meaningful and interpretable way. By synergizing the domain knowledge into development and analysis processes, we have developed a comprehensive tool that supports a seamless patient-to-patient analysis, from an overview of the patient population in the similarity space to the detailed views of genes. The system consists of multiple components enabling the complete analysis process, including data mining, interactive visualization, analytical views, and gene comparison. We demonstrate our approach with medical scientists on a case study of childhood cancer patients on how they use the tool to confirm existing hypotheses and to discover new scientific insights.

The stock market is an essential sub-sector in the financial area. Both understanding and evaluating the mountains of collected stock data has become a challenge in relevant fields. Data visualisation techniques can offer a practical and engaging method to show the processed data in a meaningful way, with centrality measurements representing the significant variables in a network, through exploring the aspects of the exact definition of the metric. Here, in this study, we conducted an approach that combines data processing, graph visualisation and social network analysis methods, to develop deeper insights of complex stock data, with the ultimate aim of drawing the correct conclusions with the finalised graph models. We addressed the performance of centrality metrics methods such as betweenness, closeness, eigenvector, PageRank and weighted degree measurements, drawing comparisons between the experiments’ results and the actual top 300 shares in the Australian Stock Market. The outcomes showed consistent results. Although, in our experiments, the results of the top 300 stocks from those five centrality measurements’ rankings did not match the top 300 shares given by the ASX (Australian Securities Exchange) entirely, in which the weighted degree and PageRank metrics performed better than other three measurements such as betweenness, closeness and eigenvector. Potential reasons may include that we did not take into account the factor of stock’s market capitalisation in the methodology. This study only considers the stock price’s changing rates among every two shares and provides a relevant static pattern at this stage. Further research will include looking at cycles and symmetry in the stock market over chosen trading days, and these may assist stakeholder in grasping deep insights of those stocks.

Virus outbreaks are threats to humanity, and coronaviruses are the latest of many epidemics in the last few decades in the world. SARS-CoV (Severe Acute Respiratory Syndrome Associated Coronavirus) is a member of the coronavirus family, so its study is useful for relevant virus data research. In this work, we conduct a proposed approach that is non-medical/clinical, generate graphs from five features of the SARS outbreak data in five countries and regions, and offer insights from a visual analysis perspective. The results show that prevention measures such as quarantine are the most common control policies used, and areas with strict measures did have fewer peak period days; for instance, Hong Kong handled the outbreak better than other areas. Data conflict issues found with this approach are discussed as well. Visual analysis is also proved to be a useful technique to present the SARS outbreak data at this stage; furthermore, we are proceeding to apply a similar methodology with more features to future COVID-19 research from a visual analysis perfective.

Graphs are often used to model data with a relational structure and graphs are usually visualised into node-link diagrams for a better understanding of the underlying data. Node-link diagrams represent not only data entries in a graph, but also the relations among the data entries. Further, many graph drawing algorithms and graph centrality metrics have been successfully applied in visual analytics of various graph datasets, yet little attention has been paid to analytics of scientific standard data. This study attempts to adopt graph drawing methods (force-directed algorithms) to visualise scientific standard data and provide information with importance ‘ranking’ based on graph centrality metrics such as Weighted Degree, PageRank, Eigenvector, Betweenness and Closeness factors. The outcomes show that our method can produce clear graph layouts of scientific standard for visual analytics, along with the importance ‘ranking’ factors (represent via node colour, size etc.). Our method may assist users with tracking various relationships while understanding scientific standards with fewer relation issues (missing/wrong connection etc.) through focusing on higher priority standards.