Our growing knowledge about various molecular mechanisms is becoming increasingly more structured and accessible. Different repositories of molecular interactions and available literature enable construction of focused and high-quality molecular interaction networks. Novel tools for curation and exploration of such networks are needed, in order to foster the development of a systems biology environment. In particular, solutions for visualization, annotation and data cross-linking will facilitate usage of network-encoded knowledge in biomedical research. To this end we developed the MINERVA (Molecular Interaction NEtwoRks VisuAlization) platform, a standalone webservice supporting curation, annotation and visualization of molecular interaction networks in Systems Biology Graphical Notation (SBGN)-compliant format. MINERVA provides automated content annotation and verification for improved quality control. The end users can explore and interact with hosted networks, and provide direct feedback to content curators. MINERVA enables mapping drug targets or overlaying experimental data on the visualized networks. Extensive export functions enable downloading areas of the visualized networks as SBGN-compliant models for efficient reuse of hosted networks. The software is available under Affero GPL 3.0 as a Virtual Machine snapshot, Debian package and Docker instance at http://r3lab.uni.lu/web/minerva-website/. We believe that MINERVA is an important contribution to systems biology community, as its architecture enables set-up of locally or globally accessible SBGN-oriented repositories of molecular interaction networks. Its functionalities allow overlay of multiple information layers, facilitating exploration of content and interpretation of data. Moreover, annotation and verification workflows of MINERVA improve the efficiency of curation of networks, allowing life-science researchers to better engage in development and use of biomedical knowledge repositories.

This article proposes a semi-interactive system for visual data exploration using an iterative clustering that combines an automatic approach with an interactive one. We propose a framework to improve the interactivity between the user and the data analysis process, allowing him or her to participate actively in the iterative clustering tasks using a two-dimensional projection. Defining a cluster by its seed (center) and its limit, the proposed approach allows the user to modify the automated values or to define new seeds and the associated cluster limit himself or herself. The user can perform the clustering according to his or her visual perception manually and can also choose to let the automated approach find optimal seeds and then interact with the process to iterate the clustering process according to his or her visual perception and domain knowledge. Most of the evaluation criteria for clustering evaluate the complete clustering and not each cluster separately. In this article, we propose to adapt evaluation criteria to single clusters, allowing the users to evaluate their own clusters and perform the clustering iteratively until satisfaction. To evaluate our proposed approach, we conduct a user evaluation, where the users are asked to perform clustering interactively according to their visual perception and with the semi-interactive one. We also compare the obtained results with those of automated clustering. The quantitative results have shown that the cooperative approach can improve the clustering results in terms of accuracy.

Modelling relationship between entities in real‐world systems with a simple graph is a standard approach. However, reality is better embraced as several interdependent subsystems (or layers). Recently, the concept of a multilayer network model has emerged from the field of complex systems. This model can be applied to a wide range of real‐world data sets. Examples of multilayer networks can be found in the domains of life sciences, sociology, digital humanities and more. Within the domain of graph visualization, there are many systems which visualize data sets having many characteristics of multilayer graphs. This report provides a state of the art and a structured analysis of contemporary multilayer network visualization, not only for researchers in visualization, but also for those who aim to visualize multilayer networks in the domain of complex systems, as well as those developing systems across application domains. We have explored the visualization literature to survey visualization techniques suitable for multilayer graph visualization, as well as tools, tasks and analytic techniques from within application domains. This report also identifies the outstanding challenges for multilayer graph visualization and suggests future research directions for addressing them.

International audienceThis paper describes Cluster Sculptor, a novel interactive clustering system that allows a user to iteratively update the cluster labels of a data set, and an as-sociated low-dimensional projection. The system is fed by clustering results computed in a high-dimensional space, and uses a 2D projection, both as sup-port for overlaying the cluster labels, and engaging user interaction. By easily interacting with elements directly in the visualization, the user can inject his or her domain knowledge progressively, crafting an updated 2D projection and the associated clustering structure that combine his or her preferences and the manifolds underlying the data. Via interactive controls, the distribution of the data in the 2D space can be used to amend the cluster labels, or reciprocally, the 2D projection can be updated so as to emphasize the current clusters. The 2D projection updates follow a smooth physical metaphor, that gives insight of the process to the user. Updates can be interrupted any time, for further data inspection, or modifying the input preferences. The interest of the system is demonstrated by detailed experimental scenarios on three real data sets

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