Fast training of self organizing maps for the visual exploration of molecular compounds

Visual exploration of scientific data in life science area is a growing research field due to the large amount of available data. The Kohonen’s Self Organizing Map (SOM) is a widely used tool for visualization of multidimensional data. In this paper we present a fast learning algorithm for SOMs that uses a simulated annealing method to adapt the learning parameters. The algorithm has been adopted in a data analysis framework for the generation of similarity maps. Such maps provide an effective tool for the visual exploration of large and multi-dimensional input spaces. The approach has been applied to data generated during the High Throughput Screening of molecular compounds; the generated maps allow a visual exploration of molecules with similar topological properties. The experimental analysis on real world data from the National Cancer Institute shows the speed up of the proposed SOM training process in comparison to a traditional approach. The resulting visual landscape groups molecules with similar chemical properties in densely connected regions

An algorithm for quantifying dependence in multivariate data sets

We describe an algorithm to quantify dependence in a multivariate data set. The algorithm is able to identify any linear and non-linear dependence in the data set by performing a hypothesis test for two variables being independent. As a result we obtain a reliable measure of dependence. In high energy physics understanding dependencies is especially important in multidimensional maximum likelihood analyses. We therefore describe the problem of a multidimensional maximum likelihood analysis applied on a multivariate data set with variables that are dependent on each other. We review common procedures used in high energy physics and show that general dependence is not the same as linear correlation and discuss their limitations in practical application. Finally we present the tool CAT, which is able to perform all reviewed methods in a fully automatic mode and creates an analysis report document with numeric results and visual review.Comment: 4 pages, 3 figure

MetricsVis: A Visual Analytics Tool for Evaluating Multidimensional Data

Visualization for multidimensional data is a popular topic and many methods have been created to visualize this type of data. We developed a visual analytics tool to visualize multidimensional data for two distinct fields: resource allocation in law enforcement departments and phenotype traits of sorghum crops. For law enforcement departments, we designed a visualization tool to measure and compare police officer’s experience in different types of crimes. Our tool supports the analysis of the amount of experience each officer has in each crime category. Meanwhile, the field crop modeling project requires the visualization of the measured value of multiple traits of each sorghum category. In general, our visualization tool is now able to represent these multidimensional data in multiple graphs and charts, with a rich interaction set of selecting, grouping, and filtering. MetricsVis has been expanded this summer with the addition of 6 new graphs, the ability to use the sorghum crops dataset, and more data manipulation features. By being able to explore the data through several graphs and charts at the same time, this allows the user to easily query the data or find peculiarities in the data that they would have otherwise missed. We describe several case studies to validate the importance of our tool in analyzing the data in both projects. In the future, we would like to expand our tool for other similar datasets

Multidimensional Data Visual Exploration by Interactive Information Segments

Visualization techniques provide an outstanding role in KDD process for data analysis and mining. However, one image does not always convey successfully the inherent information from high dimensionality, very large databases. In this paper we introduce VSIS (Visual Set of Information Segments), an interactive tool to visually explore multidimensional, very large, numerical data. Within the supervised learning, our proposal approaches the problem of classification by searching of meaningful intervals belonging to the most relevant attributes. These intervals are displayed as multi–colored bars in which the degree of impurity with respect to the class membership can be easily perceived. Such bars can be re–explored interactively with new values of user–defined parameters. A case study of applying VSIS to some UCI repository data sets shows the usefulness of our tool in supporting the exploration of multidimensional and very large data

Exploring and linking biomedical resources through multidimensional semantic spaces

Background The semantic integration of biomedical resources is still a challenging issue which is required for effective information processing and data analysis. The availability of comprehensive knowledge resources such as biomedical ontologies and integrated thesauri greatly facilitates this integration effort by means of semantic annotation, which allows disparate data formats and contents to be expressed under a common semantic space. In this paper, we propose a multidimensional representation for such a semantic space, where dimensions regard the different perspectives in biomedical research (e.g., population, disease, anatomy and protein/genes). Results This paper presents a novel method for building multidimensional semantic spaces from semantically annotated biomedical data collections. This method consists of two main processes: knowledge and data normalization. The former one arranges the concepts provided by a reference knowledge resource (e.g., biomedical ontologies and thesauri) into a set of hierarchical dimensions for analysis purposes. The latter one reduces the annotation set associated to each collection item into a set of points of the multidimensional space. Additionally, we have developed a visual tool, called 3D-Browser, which implements OLAP-like operators over the generated multidimensional space. The method and the tool have been tested and evaluated in the context of the Health-e-Child (HeC) project. Automatic semantic annotation was applied to tag three collections of abstracts taken from PubMed, one for each target disease of the project, the Uniprot database, and the HeC patient record database. We adopted the UMLS Meta-thesaurus 2010AA as the reference knowledge resource. Conclusions Current knowledge resources and semantic-aware technology make possible the integration of biomedical resources. Such an integration is performed through semantic annotation of the intended biomedical data resources. This paper shows how these annotations can be exploited for integration, exploration, and analysis tasks. Results over a real scenario demonstrate the viability and usefulness of the approach, as well as the quality of the generated multidimensional semantic spaces

Joint mapping of genes and conditions via multidimensional unfolding analysis

<p>Abstract</p> <p>Background</p> <p>Microarray compendia profile the expression of genes in a number of experimental conditions. Such data compendia are useful not only to group genes and conditions based on their similarity in overall expression over profiles but also to gain information on more subtle relations between genes and conditions. Getting a clear visual overview of all these patterns in a single easy-to-grasp representation is a useful preliminary analysis step: We propose to use for this purpose an advanced exploratory method, called multidimensional unfolding.</p> <p>Results</p> <p>We present a novel algorithm for multidimensional unfolding that overcomes both general problems and problems that are specific for the analysis of gene expression data sets. Applying the algorithm to two publicly available microarray compendia illustrates its power as a tool for exploratory data analysis: The unfolding analysis of a first data set resulted in a two-dimensional representation which clearly reveals temporal regulation patterns for the genes and a meaningful structure for the time points, while the analysis of a second data set showed the algorithm's ability to go beyond a mere identification of those genes that discriminate between different patient or tissue types.</p> <p>Conclusion</p> <p>Multidimensional unfolding offers a useful tool for preliminary explorations of microarray data: By relying on an easy-to-grasp low-dimensional geometric framework, relations among genes, among conditions and between genes and conditions are simultaneously represented in an accessible way which may reveal interesting patterns in the data. An additional advantage of the method is that it can be applied to the raw data without necessitating the choice of suitable genewise transformations of the data.</p

Visualization and Human-Machine Interaction

The digital age offers a lot of challenges in the eld of visualization. Visual imagery has been effectively used to communicate messages through the ages, to express both abstract and concrete ideas. Today, visualization has ever-expanding applications in science, engineering, education, medicine, entertainment and many other areas. Different areas of research contribute to the innovation in the eld of interactive visualization, such as data science, visual technology, Internet of things and many more. Among them, two areas of renowned importance are Augmented Reality and Visual Analytics. This thesis presents my research in the fields of visualization and human-machine interaction. The purpose of the proposed work is to investigate existing solutions in the area of Augmented Reality (AR) for maintenance. A smaller section of this thesis presents a minor research project on an equally important theme, Visual Analytics. Overall, the main goal is to identify the most important existing problems and then design and develop innovative solutions to address them. The maintenance application domain has been chosen since it is historically one of the first fields of application for Augmented Reality and it offers all the most common and important challenges that AR can arise, as described in chapter 2. Since one of the main problem in AR application deployment is reconfigurability of the application, a framework has been designed and developed that allows the user to create, deploy and update in real-time AR applications. Furthermore, the research focused on the problems related to hand-free interaction, thus investigating the area of speech-recognition interfaces and designing innovative solutions to address the problems of intuitiveness and robustness of the interface. On the other hand, the area of Visual Analytics has been investigated: among the different areas of research, multidimensional data visualization, similarly to AR, poses specific problems related to the interaction between the user and the machine. An analysis of the existing solutions has been carried out in order to identify their limitations and to point out possible improvements. Since this analysis delineates the scatterplot as a renowned visualization tool worthy of further research, different techniques for adapting its usage to multidimensional data are analyzed. A multidimensional scatterplot has been designed and developed in order to perform a comparison with another multidimensional visualization tool, the ScatterDice. The first chapters of my thesis describe my investigations in the area of Augmented Reality for maintenance. Chapter 1 provides definitions for the most important terms and an introduction to AR. The second chapter focuses on maintenance, depicting the motivations that led to choose this application domain. Moreover, the analysis concerning open problems and related works is described along with the methodology adopted to design and develop the proposed solutions. The third chapter illustrates how the adopted methodology has been applied in order to assess the problems described in the previous one. Chapter 4 describes the methodology adopted to carry out the tests and outlines the experimental results, whereas the fifth chapter illustrates the conclusions and points out possible future developments. Chapter 6 describes the analysis and research work performed in the eld of Visual Analytics, more specifically on multidimensional data visualizations. Overall, this thesis illustrates how the proposed solutions address common problems of visualization and human-machine interaction, such as interface de- sign, robustness of the interface and acceptance of new technology, whereas other problems are related to the specific research domain, such as pose tracking and reconfigurability of the procedure for the AR domain

PerfVis: Pervasive Visualization in Immersive AugmentedReality for Performance Awareness

Developers are usually unaware of the impact of code changes to the performance of software systems. Although developers can analyze the performance of a system by executing, for instance, a performance test to compare the performance of two consecutive versions of the system, changing from a programming task to a testing task would disrupt the development flow. In this paper, we propose the use of a city visualization that dynamically provides developers with a pervasive view of the continuous performance of a system. We use an immersive augmented reality device (Microsoft HoloLens) to display our visualization and extend the integrated development environment on a computer screen to use the physical space. We report on technical details of the design and implementation of our visualization tool, and discuss early feedback that we collected of its usability. Our investigation explores a new visual metaphor to support the exploration and analysis of possibly very large and multidimensional performance data. Our initial result indicates that the city metaphor can be adequate to analyze dynamic performance data on a large and non-trivial software system.Comment: ICPE'19 vision, 4 pages, 2 figure, conferenc

Development and validation of a visual grading scale for assessing image quality of AP pelvis radiographic images

OBJECTIVE: Apply psychometric theory to develop and validate a visual grading scale for assessing visual perception of AP pelvis digital image quality. METHODS: Psychometric theory was used to guide scale development. Seven phantom and 7 cadaver images of visually and objectively predetermined quality were used to help assess scale reliability and validity. 151 volunteers scored phantom images; 184 volunteers scored cadaver images. Factor analysis and Cronbach’s alpha were used to assess scale validity and reliability. RESULTS: A 24 item scale was produced. Aggregated mean volunteer scores for each image correlated with the rank order of the visually and objectively predetermined image qualities. Scale items had good inter-item correlation (≥0.2) and high factor loadings (≥0.3). Cronbach's alpha (reliability) revealed that the scale has acceptable levels of internal reliability for both phantom and cadaver images (α= 0.8 and 0.9, respectively). Factor analysis suggested the scale is multidimensional (assessing multiple quality themes). CONCLUSION: This study represents the first full development and validation of a visual image quality scale using psychometric theory. It is likely that this scale will have clinical, training and research applications. ADVANCES IN KNOWLEDGE: This article presents data to create and validate visual grading scales for radiographic examinations. The visual grading scale, for AP pelvis examinations, can act as a validated tool for future research, teaching and clinical evaluations of image quality