A Meta-Model Integration for Supporting Knowledge Discovery in Specific Domains: A Case Study in Healthcare.

Knowledge management is one of the key priorities of many organizations. They face different challenges in the implementation of knowledge management processes, including the transformation of tacit knowledge-experience, skills, insights, intuition, judgment and know-how-into explicit knowledge. Furthermore, the increasing number of information sources and services in some domains, such as healthcare, increase the amount of information available. Therefore, there is a need to transform that information in knowledge. In this context, learning ecosystems emerge as solutions to support knowledge management in a different context. On the other hand, the dashboards enable the generation of knowledge through the exploitation of the data provided from different sources. The model-driven development of these solutions is possible through two meta-models developed in previous works. Even though those meta-models solve several problems, the learning ecosystem meta-model has a lack of decision-making support. In this context, this work provides two main contributions to face this issue. First, the definition of a holistic meta-model to support decision-making processes in ecosystems focused on knowledge management, also called learning ecosystems. The second contribution of this work is an instantiation of the presented holistic meta-model in the healthcare domain.

Taking advantage of the software product line paradigm to generate customized user interfaces for decision-making processes: a case study on university employability.

University employment and, specifically, employability has gained relevance since research in these fields can lead to improvement in the quality of life of individual citizens. However, empirical research is still insufficient to make significant decisions, and relying on powerful tools to explore data and reach insights on these fields is paramount. Information dashboards play a key role in analyzing and visually exploring data about a specific topic or domain, but end users can present several necessities that differ from each other, regarding the displayed information itself, design features and even functionalities. By applying a domain engineering approach (within the software product line paradigm), it is possible to produce customized dashboards to fit into particular requirements, by the identification of commonalities and singularities of every product that could be part of the product line. Software product lines increase productivity, maintainability and traceability regarding the evolution of the requirements, among other benefits. To validate this approach, a case study of its application in the context of the Spanish Observatory for University Employability and Employment system has been developed, where users (Spanish universities and administrators) can control their own dashboards to reach insights about the employability of their graduates. These dashboards have been automatically generated through a domain specific language, which provides the syntax to specify the requirements of each user. The domain language fuels a template-based code generator, allowing the generation of the dashboards' source code. Applying domain engineering to the dashboards' domain improves the development and maintainability of these complex software products given the variety of requirements that users might have regarding their graphical interfaces.

Genome-wide search of nucleosome patterns using visual analytics.

MOTIVATION: The Burrows-Wheeler transform (BWT) is widely used for the fast alignment of high-throughput sequence data. This method also has potential applications in other areas of bioinformatics, and it can be specially useful for the fast searching of patterns on coverage data from different sources. RESULTS: We present a nucleosome pattern search method that converts levels of nucleosomal occupancy to a sequence-like format to which BWT searches can be applied. The method is embedded in a nucleosome map browser, 'Nucleosee', an interactive visual tool specifically designed to enhance BWT searches, giving them context and making them suitable for visual discourse analysis of the results. The proposed method is fast, flexible and sufficiently generic for the exploration of data in a broad and interactive way. AVAILABILITY AND IMPLEMENTATION: The proposed algorithm and visual browser are available for testing at http://cpg3.der.usal.es/nucleosee. The source code and installation packages are also available at https://github.com/rodrigoSantamaria/nucleosee. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Data-Driven Visual Performance Analysis in Soccer: An Exploratory Prototype.

In soccer, understanding of collective tactical behavior has become an integral part in sports analysis at elite levels. Evolution of technology allows collection of increasingly larger and more specific data sets related to sport activities in cost-effective and accessible manner. All this information is minutely scrutinized by thousands of analysts around the globe in search of answers that can in the long-term help increase the performance of individuals or teams in their respective competitions. As the volume of data increases in size, so does the complexity of the problem and the need for suitable tools that leverage the cognitive load involved in the investigation. It is proven that visualization and computer-vision techniques, correctly applied to the context of a problem, help data analysts focus on the relevant information at each stage of the process, and generally lead to a better understanding of the facts that lie behind the data. In the current study, we presented a software prototype capable of assisting researchers and performance analysts in their duty of studying group collective behavior in soccer games and trainings. We used geospatial data acquired from a professional match to demonstrate its capabilities in two different case studies. Furthermore, we successfully proved the efficiency of the different visualization techniques implemented in the prototype and demonstrated how visual analysis can effectively improve some of the basic tasks employed by sports experts on their daily work, complementing more traditional approaches.

JADOPPT: java based AutoDock preparing and processing tool.

Motivation: AutoDock is a very popular software package for docking and virtual screening. However, currently it is hard work to visualize more than one result from the virtual screening at a time. To overcome this limitation we have designed JADOPPT, a tool for automatically preparing and processing multiple ligand-protein docked poses obtained from AutoDock. It allows the simultaneous visual assessment and comparison of multiple poses through clustering methods. Moreover, it permits the representation of reference ligands with known binding modes, binding site residues, highly scoring regions for the ligand, and the calculated binding energy of the best ranked results. Availability and Implementation: JADOPPT, supplementary material (Case Studies 1 and 2) and video tutorials are available at http://visualanalytics.land/cgarcia/JADOPPT.html. Contacts: carlosgarcia@usal.es or pelaez@usal.es. Supplementary information: Supplementary data are available at Bioinformatics online.

BKViz: A Basketball Visual Analysis Tool.

The amount of data available in the sports field is difficult for coaches, analysts, and players to comprehend using classic analytics methods. Thus, new methods are necessary to help users break down that information and analyze it at a deeper level. The BKViz visual analytics system focuses on individual basketball games using classic and novel methods to reveal how players perform together and as individuals. The information is presented in interactive visualizations that allow immediate user feedback.

BicOverlapper 2.0: visual analysis for gene expression.

MOTIVATION: Systems biology demands the use of several point of views to get a more comprehensive understanding of biological problems. This usually leads to take into account different data regarding the problem at hand, but it also has to do with using different perspectives of the same data. This multifaceted aspect of systems biology often requires the use of several tools, and it is often hard to get a seamless integration of all of them, which would help the analyst to have an interactive discourse with the data. RESULTS: Focusing on expression profiling, BicOverlapper 2.0 visualizes the most relevant aspects of the analysis, including expression data, profiling analysis results and functional annotation. It also integrates several state-of-the-art numerical methods, such as differential expression analysis, gene set enrichment or biclustering. AVAILABILITY AND IMPLEMENTATION: BicOverlapper 2.0 is available at: http://vis.usal.es/bicoverlapper2

Treevolution: visual analysis of phylogenetic trees.

SUMMARY: Treevolution is a tool for the representation and exploration of phylogenetic trees that facilitates visual analysis. There are several useful tools to visualize phylogenetic trees, but their level of interaction is usually low, especially in the case of radial representations. Highly interactive visualizations can improve the exploration and understanding of phylogenetic trees. Treevolution implements strategies to interact with phylogenetic trees in order to allow a more thorough analysis by users. AVAILABILITY: Treevolution is available at http://vis.usal.es/treevolution. Additional figures, a user's guide, a video demo and some examples are available at the same site. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

A visual analytics approach for understanding biclustering results from microarray data.

BACKGROUND: Microarray analysis is an important area of bioinformatics. In the last few years, biclustering has become one of the most popular methods for classifying data from microarrays. Although biclustering can be used in any kind of classification problem, nowadays it is mostly used for microarray data classification. A large number of biclustering algorithms have been developed over the years, however little effort has been devoted to the representation of the results. RESULTS: We present an interactive framework that helps to infer differences or similarities between biclustering results, to unravel trends and to highlight robust groupings of genes and conditions. These linked representations of biclusters can complement biological analysis and reduce the time spent by specialists on interpreting the results. Within the framework, besides other standard representations, a visualization technique is presented which is based on a force-directed graph where biclusters are represented as flexible overlapped groups of genes and conditions. This microarray analysis framework (BicOverlapper), is available at http://vis.usal.es/bicoverlapper CONCLUSION: The main visualization technique, tested with different biclustering results on a real dataset, allows researchers to extract interesting features of the biclustering results, especially the highlighting of overlapping zones that usually represent robust groups of genes and/or conditions. The visual analytics methodology will permit biology experts to study biclustering results without inspecting an overwhelming number of biclusters individually.

BicOverlapper: a tool for bicluster visualization.

UNLABELLED: BicOverlapper is a tool to visualize biclusters from gene-expression matrices in a way that helps to compare biclustering methods, to unravel trends and to highlight relevant genes and conditions. A visual approach can complement biological and statistical analysis and reduce the time spent by specialists interpreting the results of biclustering algorithms. The technique is based on a force-directed graph where biclusters are represented as flexible overlapped groups of genes and conditions. AVAILABILITY: The BicOverlapper software and supplementary material are available at http://vis.usal.es/bicoverlapper

NAPROC-13: a database for the dereplication of natural product mixtures in bioassay-guided protocols.

MOTIVATION: Although natural products represent a reservoir of molecular diversity, the process of isolating and identifying active compounds is a bottleneck in drug discovery programs. The rapid isolation and identification of the bioactive component(s) of natural product mixtures during the bioassay-guided fractionation have become crucial factors in the competition with chemical compound libraries and combinatorial synthetic efforts. In this respect, the use of spectral databases in identification processes is indispensable. RESULTS: We have developed a database containing (13)C spectral information of over 6000 natural compounds, which allows for fast identifications of known compounds present in the crude extracts and provides insight into the structural elucidation of unknown compounds. AVAILABILITY: http://c13.usal.es