Do redundant graphical attributes reduce decision-making efficiency?

Multiple time-series graphs are commonly used for data visualisation, but few scholars have investigated the impact of graphical attributes on decision-making efficiency. This study explores the effects of graphical attributes of varying redundancy conditions on decision-making efficiency. Two experimental conditions were developed for the experiment: non-redundant (independent graphical attributes: colour, linear and marker) and redundant (combinations of two and more graphical attributes: colour and linear, colour and marker, etc.). A total of 60 people took part in both experiments and performed two tasks: maximisation and discrimination. The experiments revealed that the addition of attributes, such as colour, marker or linear, decreased response time (RT), but the combination of colour & linear & marker increased RT. This is more significant in discrimination tasks. We provide empirical evidence for the design of time-series data visualisations and encourage the combination of two of these graphical attributes, such as colour & linear, colour & marker or linear & marker, when conditions allow, to improve decision-making efficiency.

Few scholars have studied the impact of graphical attributes on decision-making efficiency in data visualisation. This study explores the effect of graphical attributes with different redundancy levels on decision-making efficiency through behavioural experiments. It has been found that moderately redundant graphical attributes in difficult tasks can significantly improve decision-making efficiency.

A bibliometric study: Relevant studies on scar laser therapy since the 21st century.

To summarise research studies on scar laser therapy since the 21st century using bibliometric methods, and to speculate on the possible development in the future. The literature about scar laser therapy in Web of Science database was searched. CiteSpace and VOSviewer were used to analyse main countries, institutions, journals，subject hotspots and trends, etc. A total of 884 papers have been published since the 21st century. These publications were written by 653 authors from 515 institutions in 58 countries. The United States published 287 papers in this field and ranks first. Laser in Surgery and Medicine is the most widely published journal, with Shumaker as the core author. The main keyword clustering includes terms such as combination therapy, wound healing, fractional photothermolysis, experience, scar formation, etc. CiteSpace and VOSviewer were used to sort out and summarise the countries, institutions, authors, journals, research hotspots and frontier topics of related literature about scar laser therapy since the 21st century. The current situation of its application and basic scientific research in clinical treatments were summarised briefly. This provides a new idea for the development and research of scar laser therapy in the future.

A new pathway for considering trigger factors based on parallel-serial connection models and displaying the relationships of causal factors in low-probability events.

BACKGROUND: To determine the effect size of observed factors considering trigger factors based on parallel-serial models and to explore how multiple factors can be related to the result of complex events for low-probability events with binary outcomes. METHODS: A low-probability event with a true binary outcome can be explained by a trigger factor. The models were based on the parallel-serial connection of switches; causal factors, including trigger factors, were simplified as switches. Effect size values of an observed factor for an outcome were calculated as SAR = (Pe-Pn)/(Pe + Pn), where Pe and Pn represent percentages in the exposed and nonexposed groups, respectively, and SAR represents standardized absolute risk. The influence of trigger factors is eliminated by SAR. Actual data were collected to obtain a deeper understanding of the system. RESULTS: SAR values of < 0.25, 0.25-0.50, and > 0.50 indicate low, medium, and high effect sizes, respectively. The system of data visualization based on the parallel-serial connection model revealed that at least 7 predictors with SAR > 0.50, including a trigger factor, were needed to predict schizophrenia. The SAR of the HLADQB1*03 gene was 0.22 for schizophrenia. CONCLUSIONS: It is likely that the trigger factors and observed factors had a cumulative effect, as indicated by the parallel-serial connection model for binary outcomes. SAR may allow better evaluation of the effect size of a factor in complex events by eliminating the influence of trigger factors. The efficiency and efficacy of observational research could be increased if we are able to clarify how multiple factors can be related to a result in a pragmatic manner.

Data visualisation approaches for component network meta-analysis: visualising the data structure.

BACKGROUND: Health and social care interventions are often complex and can be decomposed into multiple components. Multicomponent interventions are often evaluated in randomised controlled trials. Across trials, interventions often have components in common which are given alongside other components which differ across trials. Multicomponent interventions can be synthesised using component NMA (CNMA). CNMA is limited by the structure of the available evidence, but it is not always straightforward to visualise such complex evidence networks. The aim of this paper is to develop tools to visualise the structure of complex evidence networks to support CNMA. METHODS: We performed a citation review of two key CNMA methods papers to identify existing published CNMA analyses and reviewed how they graphically represent intervention complexity and comparisons across trials. Building on identified shortcomings of existing visualisation approaches, we propose three approaches to standardise visualising the data structure and/or availability of data: CNMA-UpSet plot, CNMA heat map, CNMA-circle plot. We use a motivating example to illustrate these plots. RESULTS: We identified 34 articles reporting CNMAs. A network diagram was the most common plot type used to visualise the data structure for CNMA (26/34 papers), but was unable to express the complex data structures and large number of components and potential combinations of components associated with CNMA. Therefore, we focused visualisation development around representing the data structure of a CNMA more completely. The CNMA-UpSet plot presents arm-level data and is suitable for networks with large numbers of components or combinations of components. Heat maps can be utilised to inform decisions about which pairwise interactions to consider for inclusion in a CNMA model. The CNMA-circle plot visualises the combinations of components which differ between trial arms and offers flexibility in presenting additional information such as the number of patients experiencing the outcome of interest in each arm. CONCLUSIONS: As CNMA becomes more widely used for the evaluation of multicomponent interventions, the novel CNMA-specific visualisations presented in this paper, which improve on the limitations of existing visualisations, will be important to aid understanding of the complex data structure and facilitate interpretation of the CNMA results.

Evaluating the harmonisation potential of diverse cohort datasets.

Data discovery, the ability to find datasets relevant to an analysis, increases scientific opportunity, improves rigour and accelerates activity. Rapid growth in the depth, breadth, quantity and availability of data provides unprecedented opportunities and challenges for data discovery. A potential tool for increasing the efficiency of data discovery, particularly across multiple datasets is data harmonisation.A set of 124 variables, identified as being of broad interest to neurodegeneration, were harmonised using the C-Surv data model. Harmonisation strategies used were simple calibration, algorithmic transformation and standardisation to the Z-distribution. Widely used data conventions, optimised for inclusiveness rather than aetiological precision, were used as harmonisation rules. The harmonisation scheme was applied to data from four diverse population cohorts.Of the 120 variables that were found in the datasets, correspondence between the harmonised data schema and cohort-specific data models was complete or close for 111 (93%). For the remainder, harmonisation was possible with a marginal a loss of granularity.Although harmonisation is not an exact science, sufficient comparability across datasets was achieved to enable data discovery with relatively little loss of informativeness. This provides a basis for further work extending harmonisation to a larger variable list, applying the harmonisation to further datasets, and incentivising the development of data discovery tools.

The World Organisation for Animal Health Observatory: a data-driven approach to address the needs of its Members.

This article reviews the Observatory of the World Organisation for Animal Health (WOAH, founded as OIE), including its objectives, direction and progress achieved so far. It explains the benefits offered by this data-driven programme in improving access to data and information analysis while ensuring confidentiality. In addition, the authors examine the challenges that the Observatory faces and its inextricable link to the Organisation's data management. The development of the Observatory is of the utmost importance, not only for its relevance to the development of WOAH International Standards and their implementation worldwide, but also because of its role as one of the drivers of WOAH's digital transformation plan. This transformation is essential, given the major role of information technologies in supporting regulation for animal health, animal welfare and veterinary public health.

Cet article consacré à l'Observatoire de l'Organisation mondiale de la santé animale (OMSA, fondée en tant qu'OIE) fait le point sur ses objectifs, ses orientations et les accomplissements réalisés à ce jour. Les auteurs expliquent les bénéfices et améliorations apportés par ce programme axé sur les données, en termes à la fois d'accès aux données, d'analyse de l'information et de confidentialité. Ils abordent aussi les défis auxquels l'Observatoire doit faire face et son lien indissociable avec la gestion des données de l'Organisation. Le développement de l'Observatoire revêt une importance fondamentale, non seulement en regard de sa pertinence pour l'élaboration et la mise en oeuvre des normes internationales de l'OMSA partout dans le monde, mais aussi parce qu'il est le moteur de la planification de la transformation numérique de l'OMSA. Cette transformation est cruciale, compte tenu du rôle majeur que jouent les technologies de l'information en appui de la réglementation de la santé animale, du bien-être animal et de la santé publique vétérinaire.

Los autores presentan el Observatorio de la Organización Mundial de Sanidad Animal (OMSA, fundada como OIE), deteniéndose en particular en sus objetivos, su orientación y el camino recorrido hasta ahora. También exponen las ventajas que ofrece este programa centrado en los datos para mejorar tanto el acceso a los datos como los análisis de información, asegurando al mismo tiempo la confidencialidad. Además, examinan las dificultades a que hace frente el Observatorio y el indisociable vínculo que este guarda con la gestión de datos de la Organización. El desarrollo del Observatorio reviste una importancia capital, no solo por su utilidad para la elaboración de las normas internacionales de la OMSA y su aplicación en todo el mundo, sino también porque constituye uno de los motores del plan de transformación digital de la OMSA. Se trata de un proceso de transformación esencial, dada la importante función que cumplen las tecnologías de la información a la hora de regular la sanidad y el bienestar animales y la salud pública veterinaria.

Relationships between contract status and player performance in the Australian Football League.

This study analysed the extent to which player performance differs within the Australian Football League (AFL) with respect to the status of a player's contract. AFL Player Ratings (AFLPR) and contract data were obtained during the 2013-2020 AFL seasons for all 827 players listed by an AFL club at the beginning of the 2020 season. A model of "expected performance" was created allowing for an exploration into the differential with actual performance as a function of contract status. Paired t-tests indicated that there was a difference in performance pre- and post-signing their contract for players who signed mid-season (mean change and 95% confidence interval of -1.48 ± 0.93 and -0.49 ± 0.48 AFLPR, at ten match intervals for those in- and out-of-contract at the conclusion of that year's season, respectively). Further differences existed between the groups of players who signed mid-season, as compared to those who signed during the off-season. Correlation analyses indicated that more consistent performers are somewhat less likely to see a reduction in performance post signing as compared to less consistent performers. The applications of these findings have the potential to support organisational decisions relating to the timing and nature of player contracting.

Protocol for developing a dashboard for interactive cohort analysis of oral health-related data.

INTRODUCTION: A working knowledge of data analytics is becoming increasingly important in the digital health era. Interactive dashboards are a useful, accessible format for presenting and disseminating health-related information to a wide audience. However, many oral health researchers receive minimal data visualisation and programming skills. OBJECTIVES: The objective of this protocols paper is to demonstrate the development of an analytical, interactive dashboard, using oral health-related data from multiple national cohort surveys. METHODS: The flexdashboard package was used within the R Studio framework to create the structure-elements of the dashboard and interactivity was added with the Shiny package. Data sources derived from the national longitudinal study of children in Ireland and the national children's food survey. Variables for input were selected based on their known associations with oral health. The data were aggregated using tidyverse packages such as dplyr and summarised using ggplot2 and kableExtra with specific functions created to generate bar-plots and tables. RESULTS: The dashboard layout is structured by the YAML (YAML Ain't Markup Language) metadata in the R Markdown document and the syntax from Flexdashboard. Survey type, wave of survey and variable selector were set as filter options. Shiny's render functions were used to change input to automatically render code and update output. The deployed dashboard is openly accessible at https://dduh.shinyapps.io/dduh/ . Examples of how to interact with the dashboard for selected oral health variables are illustrated. CONCLUSION: Visualisation of national child cohort data in an interactive dashboard allows viewers to dynamically explore oral health data without requiring multiple plots and tables and sharing of extensive documentation. Dashboard development requires minimal non-standard R coding and can be quickly created with open-source software.

A graph-based approach for the visualisation and analysis of bacterial pangenomes.

BACKGROUND: The advent of low cost, high throughput DNA sequencing has led to the availability of thousands of complete genome sequences for a wide variety of bacterial species. Examining and interpreting genetic variation on this scale represents a significant challenge to existing methods of data analysis and visualisation. RESULTS: Starting with the output of standard pangenome analysis tools, we describe the generation and analysis of interactive, 3D network graphs to explore the structure of bacterial populations, the distribution of genes across a population, and the syntenic order in which those genes occur, in the new open-source network analysis platform, Graphia. Both the analysis and the visualisation are scalable to datasets of thousands of genome sequences. CONCLUSIONS: We anticipate that the approaches presented here will be of great utility to the microbial research community, allowing faster, more intuitive, and flexible interaction with pangenome datasets, thereby enhancing interpretation of these complex data.

Shape-aware stochastic neighbor embedding for robust data visualisations.

BACKGROUND: The t-distributed Stochastic Neighbor Embedding (t-SNE) algorithm has emerged as one of the leading methods for visualising high-dimensional (HD) data in a wide variety of fields, especially for revealing cluster structure in HD single-cell transcriptomics data. However, t-SNE often fails to correctly represent hierarchical relationships between clusters and creates spurious patterns in the embedding. In this work we generalised t-SNE using shape-aware graph distances to mitigate some of the limitations of the t-SNE. Although many methods have been recently proposed to circumvent the shortcomings of t-SNE, notably Uniform manifold approximation (UMAP) and Potential of heat diffusion for affinity-based transition embedding (PHATE), we see a clear advantage of the proposed graph-based method. RESULTS: The superior performance of the proposed method is first demonstrated on simulated data, where a significant improvement compared to t-SNE, UMAP and PHATE, based on quantitative validation indices, is observed when visualising imbalanced, nonlinear, continuous and hierarchically structured data. Thereafter the ability of the proposed method compared to the competing methods to create faithfully low-dimensional embeddings is shown on two real-world data sets, the single-cell transcriptomics data and the MNIST image data. In addition, the only hyper-parameter of the method can be automatically chosen in a data-driven way, which is consistently optimal across all test cases in this study. CONCLUSIONS: In this work we show that the proposed shape-aware stochastic neighbor embedding method creates low-dimensional visualisations that robustly and accurately reveal key structures of high-dimensional data.

Visualising three-dimensional genome organisation in two dimensions.

The three-dimensional organisation of the genome plays a crucial role in developmental gene regulation. In recent years, techniques to investigate this organisation have become more accessible to labs worldwide due to improvements in protocols and decreases in the cost of high-throughput sequencing. However, the resulting datasets are complex and can be challenging to analyse and interpret. Here, we provide a guide to visualisation approaches that can aid the interpretation of such datasets and the communication of biological results.

Malaria-VisAnalytics: a tool for visual exploratory analysis of Brazilian public malaria data.

BACKGROUND: Data integration and visualisation techniques have been widely used in scientific research to allow the exploitation of large volumes of data and support highly complex or long-lasting research questions. Integration allows data from different sources to be aggregated into a single database comprising variables of interest for different types of studies. Visualisation allows large and complex data sets to be manipulated and interpreted in a more intuitive way. METHODS: Integration and visualisation techniques were applied in a malaria surveillance ecosystem to build an integrated database comprising notifications, deaths, vector control and climate data. This database is accessed through Malaria-VisAnalytics, a visual mining platform for descriptive and predictive analysis supporting decision and policy-making by governmental and health agents. RESULTS: Experimental and validation results have proved that the visual exploration and interaction mechanisms allow effective surveillance for rapid action in suspected outbreaks, as well as support a set of different research questions over integrated malaria electronic health records. CONCLUSION: The integrated database and the visual mining platform (Malaria-VisAnalytics) allow different types of users to explore malaria-related data in a user-friendly interface. Summary data and key insights can be obtained through different techniques and dimensions. The case study on Manaus can serve as a reference for future replication in other municipalities. Finally, both the database and the visual mining platform can be extended with new data sources and functionalities to accommodate more complex scenarios (such as real-time data capture and analysis).

Comparison of different metrics of cerebral autoregulation in association with major morbidity and mortality after cardiac surgery.

BACKGROUND: Cardiac surgery studies have established the clinical relevance of personalised arterial blood pressure management based on cerebral autoregulation. However, variabilities exist in autoregulation evaluation. We compared the association of several cerebral autoregulation metrics, calculated using different methods, with outcomes after cardiac surgery. METHODS: Autoregulation was measured during cardiac surgery in 240 patients. Mean flow index and cerebral oximetry index were calculated as Pearson's correlations between mean arterial pressure (MAP) and transcranial Doppler blood flow velocity or near-infrared spectroscopy signals. The lower limit of autoregulation and optimal mean arterial pressure were identified using mean flow index and cerebral oximetry index. Regression models were used to examine associations of area under curve and duration of mean arterial pressure below thresholds with stroke, acute kidney injury (AKI), and major morbidity and mortality. RESULTS: Both mean flow index and cerebral oximetry index identified the cerebral lower limit of autoregulation below which MAP was associated with a higher incidence of AKI and major morbidity and mortality. Based on magnitude and significance of the estimates in adjusted models, the area under curve of MAP < lower limit of autoregulation had the strongest association with AKI and major morbidity and mortality. The odds ratio for area under the curve of MAP < lower limit of autoregulation was 1.05 (95% confidence interval, 1.01-1.09), meaning every 1 mm Hg h increase of area under the curve was associated with an average increase in the odds of AKI by 5%. CONCLUSIONS: For cardiac surgery patients, area under curve of MAP < lower limit of autoregulation using mean flow index or cerebral oximetry index had the strongest association with AKI and major morbidity and mortality. Trials are necessary to evaluate this target for MAP management.

Clustering of match running and performance indicators to assess between- and within-playing position similarity in professional rugby league.

This study aimed to determine the similarity between and within positions in professional rugby league in terms of technical performance and match displacement. Here, the analyses were repeated on 3 different datasets which consisted of technical features only, displacement features only, and a combined dataset including both. Each dataset contained 7617 observations from the 2018 and 2019 Super League seasons, including 366 players from 11 teams. For each dataset, feature selection was initially used to rank features regarding their importance for predicting a player's position for each match. Subsets of 12, 11, and 27 features were retained for technical, displacement, and combined datasets for subsequent analyses. Hierarchical cluster analyses were then carried out on the positional means to find logical groupings. For the technical dataset, 3 clusters were found: (1) props, loose forwards, second-row, hooker; (2) halves; (3) wings, centres, fullback. For displacement, 4 clusters were found: (1) second-rows, halves; (2) wings, centres; (3) fullback; (4) props, loose forward, hooker. For the combined dataset, 3 clusters were found: (1) halves, fullback; (2) wings and centres; (3) props, loose forward, hooker, second-rows. These positional clusters can be used to standardise positional groups in research investigating either technical, displacement, or both constructs within rugby league.

A data visualisation method for assessing exposure misclassification in case-crossover studies: the example of tricyclic antidepressants and the risk of hip fracture in older people.

BACKGROUND: The case-crossover design is suited to medication safety studies but is vulnerable to exposure misclassification. Using the example of tricyclic antidepressants and the risk of hip fracture, we present a data visualisation tool for observing exposure misclassification in case-crossover studies. METHODS: A case-crossover study was conducted using Australian Government Department of Veterans' Affairs claims data. Beneficiaries aged over 65 years who were hospitalised for hip fracture between 2009 and 2012 were included. The case window was defined as 1-50 days pre fracture. Control window one and control window two were defined as 101-150 and 151-200 days pre fracture, respectively. Patients were stratified by whether exposure status changed when control window two was specified instead of control window one. To visualise potential misclassification, each subject's tricyclic antidepressant dispensings were plotted over the 200 days pre fracture. RESULTS: The study population comprised 8828 patients with a median age of 88 years. Of these subjects, 348 contributed data to the analyses with either control window. The data visualisation suggested that 14% of subjects were potentially misclassified with control window one while 45% were misclassified with control window two. The odds ratio for the association between tricyclic antidepressants and hip fracture was 1.18 (95% confidence interval = 0.91-1.52) using control window one, whereas risk was significantly increased (odds ratio = 1.43, 95% confidence interval = 1.11-1.83) using control window two. CONCLUSIONS: Exposure misclassification was less likely to be present with control window one than with an earlier control window, control window two. When specifying different control windows in a case-crossover study, data visualisation can help to assess the extent to which exposure misclassification may contribute to variable results.

Google search data as a novel adjunct to patient and public involvement in rheumatology research.

Patient and public involvement is essential in the design and implementation of research studies to ensure research remains relevant and in line with public priorities. Public views on a given area of research may be sought via platforms such as focus groups or surveys. Here, we present the use of an openly available Google search data query tool, which may be used alongside traditional forms of patient and public involvement in research to highlight public perceptions and priorities. We used an online search query tool ("AnswerThePublic.com") to explore public Google searches relating to "arthritis," and an exemplar rheumatic disease, "rheumatoid arthritis." The most common searches relating to these diseases included quality of life, treatment, prognosis, as well as impacts on life, including work. However, they also reveal concerns that may be more difficult to elicit in face-to-face focus groups, such as questions on alcohol consumption in arthritis, and impacts on mental health. Using public search engine data in research, alongside the important traditional methods of patient and public involvement, is a cost-effective and time-efficient method of gauging public views and concerns on a given topic. It may facilitate broad scoping searches of public priorities and help to guide future research questions.

Apples and oranges? Comparing player performances between the Australian Football League and second-tier leagues.

This study developed a model to determine the extent to which player performance objectively differs between various Australian football (AF) leagues. Champion Data (CD) ranking points were obtained during the 2016-2019 seasons, for all players across the Australian Football League (AFL) and the 10 main second-tier AF leagues. Data pertaining to each player's age, playing position and the AF leagues in which they competed in were also collected. Phase One investigated the difference between the AFL and the senior second-tier leagues in which AFL affiliate teams participate. Post-hoc tests indicated that objective player performance was substantially different between the AFL and each of the four senior second-tier leagues (effects ranging from 16.8 to 21.6 CD ranking points). Phase Two investigated the difference between the second-tier leagues from which players are traditionally drafted by an AFL club. Post-hoc tests indicated that objective player performance was substantially different between the four senior second-tier leagues as well as the under-18 national championships, in comparison to each of the reserve and under-18 state leagues. Professional sporting organisations may utilise the methods provided here as an example of what could be implemented to support decisions regarding player contracting, recruitment and team selection.

A critical review of graphics for subgroup analyses in clinical trials.

Subgroup analyses are a routine part of clinical trials to investigate whether treatment effects are homogeneous across the study population. Graphical approaches play a key role in subgroup analyses to visualise effect sizes of subgroups, to aid the identification of groups that respond differentially, and to communicate the results to a wider audience. Many existing approaches do not capture the core information and are prone to lead to a misinterpretation of the subgroup effects. In this work, we critically appraise existing visualisation techniques, propose useful extensions to increase their utility and attempt to develop an effective visualisation approach. We focus on forest plots, UpSet plots, Galbraith plots, subpopulation treatment effect pattern plot, and contour plots, and comment on other approaches whose utility is more limited. We illustrate the methods using data from a prostate cancer study.

[Digital public health-leverage for community capacity building in health promotion : Current situation, developmental issues and TEAviisari as a model implementation]. / Digital Public Health ­ Hebel für Capacity Building in der kommunalen Gesundheitsförderung : Ausgangslage, Entwicklungsfragen, TEAviisari als modellhafte Implementierung.

In 1986, the Ottawa charter marked a paradigm shift for public health, putting the focus on strengthening community action and on creating supportive environments for health. A key to this is "capacity building" (CB), which we understand as the development and sustainable implementation of structural capacities, e.g. coordinated data collection, collaboration processes across sectors and reliable provision of basic resources in all areas of local health promotion.Many efforts and three and a half decades later we still envisage infrastructure deficits, scattered public health landscapes and restraints to intersectoral cooperation much too often. While agreement on the theoretical insights on what is needed appears to be broad, translating these insights into practice remains a challenge. In this situation, digital public health (DPH) can contribute to overcoming barriers and making knowledge for action more visible and more accessible. With DPH, data can be integrated, structured and disseminated in novel ways.We discuss why CB at the local level could benefit from technological advances and what DPH might do for the provision of information services on public health capacity. Our focus is on the web-based, interactive representation of public health data for use in information, governance or benchmarking processes. As an example from public health practice, the Finnish tool TEAviisari (National Institute for Health and Welfare, Finland) is presented.The 2020 EU Council Presidency of Germany - with the topics of digitalisation and the common European health data space - offers opportunities to decisively advance the development of CB in health promotion in this country.

Metabolic and signalling network maps integration: application to cross-talk studies and omics data analysis in cancer.

BACKGROUND: The interplay between metabolic processes and signalling pathways remains poorly understood. Global, detailed and comprehensive reconstructions of human metabolism and signalling pathways exist in the form of molecular maps, but they have never been integrated together. We aim at filling in this gap by integrating of both signalling and metabolic pathways allowing a visual exploration of multi-level omics data and study of cross-regulatory circuits between these processes in health and in disease. RESULTS: We combined two comprehensive manually curated network maps. Atlas of Cancer Signalling Network (ACSN), containing mechanisms frequently implicated in cancer; and ReconMap 2.0, a comprehensive reconstruction of human metabolic network. We linked ACSN and ReconMap 2.0 maps via common players and represented the two maps as interconnected layers using the NaviCell platform for maps exploration ( https://navicell.curie.fr/pages/maps_ReconMap%202.html ). In addition, proteins catalysing metabolic reactions in ReconMap 2.0 were not previously visually represented on the map canvas. This precluded visualisation of omics data in the context of ReconMap 2.0. We suggested a solution for displaying protein nodes on the ReconMap 2.0 map in the vicinity of the corresponding reaction or process nodes. This permits multi-omics data visualisation in the context of both map layers. Exploration and shuttling between the two map layers is possible using Google Maps-like features of NaviCell. The integrated networks ACSN-ReconMap 2.0 are accessible online and allows data visualisation through various modes such as markers, heat maps, bar-plots, glyphs and map staining. The integrated networks were applied for comparison of immunoreactive and proliferative ovarian cancer subtypes using transcriptomic, copy number and mutation multi-omics data. A certain number of metabolic and signalling processes specifically deregulated in each of the ovarian cancer sub-types were identified. CONCLUSIONS: As knowledge evolves and new omics data becomes more heterogeneous, gathering together existing domains of biology under common platforms is essential. We believe that an integrated ACSN-ReconMap 2.0 networks will help in understanding various disease mechanisms and discovery of new interactions at the intersection of cell signalling and metabolism. In addition, the successful integration of metabolic and signalling networks allows broader systems biology approach application for data interpretation and retrieval of intervention points to tackle simultaneously the key players coordinating signalling and metabolism in human diseases.