The BioDICE Taverna plugin for clustering and visualization of biological data: a workflow for molecular compounds exploration

Background: In many experimental pipelines, clustering of multidimensional biological datasets is used to detect hidden structures in unlabelled input data. Taverna is a popular workflow management system that is used to design and execute scientific workflows and aid in silico experimentation. The availability of fast unsupervised methods for clustering and visualization in the Taverna platform is important to support a data-driven scientific discovery in complex and explorative bioinformatics applications. Results: This work presents a Taverna plugin, the Biological Data Interactive Clustering Explorer (BioDICE), that performs clustering of high-dimensional biological data and provides a nonlinear, topology preserving projection for the visualization of the input data and their similarities. The core algorithm in the BioDICE plugin is Fast Learning Self Organizing Map (FLSOM), which is an improved variant of the Self Organizing Map (SOM) algorithm. The plugin generates an interactive 2D map that allows the visual exploration of multidimensional data and the identification of groups of similar objects. The effectiveness of the plugin is demonstrated on a case study related to chemical compounds. Conclusions: The number and variety of available tools and its extensibility have made Taverna a popular choice for the development of scientific data workflows. This work presents a novel plugin, BioDICE, which adds a data-driven knowledge discovery component to Taverna. BioDICE provides an effective and powerful clustering tool, which can be adopted for the explorative analysis of biological datasets.

Information mining and visualization of data from the Brazilian Supreme Court (STF): a case study

EMAp - Escola de Matemática Aplicada

An online platform for real-time sensor data collection, visualization, and sharing

Sharing sensor data between multiple devices and users can be^challenging for naive users, and requires knowledge of programming and use of different communication channels and/or development tools, leading to non uniform solutions. This thesis proposes a system that allows users to access sensors, share sensor data and manage sensors. With this system we intent to manage devices, share sensor data, compare sensor data, and set policies to act based on rules. This thesis presents the design and implementation of the system, as well as three case studies of its use.

Detection of glucose and triglycerides using information visualization methods to process impedance spectroscopy data

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

A Simpler and More Accurate AUTO-HDS Framework for Clustering and Visualization of Biological Data

In [1], the authors proposed a framework for automated clustering and visualization of biological data sets named AUTO-HDS. This letter is intended to complement that framework by showing that it is possible to get rid of a user-defined parameter in a way that the clustering stage can be implemented more accurately while having reduced computational complexity

Detection of glucose and triglycerides using information visualization methods to process impedance spectroscopy data

In this paper we discuss the detection of glucose and triglycerides using information visualization methods to process impedance spectroscopy data. The sensing units contained either lipase or glucose oxidase immobilized in layer-by-layer (LbL) films deposited onto interdigitated electrodes. The optimization consisted in identifying which part of the electrical response and combination of sensing units yielded the best distinguishing ability. It is shown that complete separation can be obtained for a range of concentrations of glucose and triglyceride when the interactive document map (IDMAP) technique is used to project the data into a two-dimensional plot. Most importantly, the optimization procedure can be extended to other types of biosensors, thus increasing the versatility of analysis provided by tailored molecular architectures exploited with various detection principles. (C) 2012 Elsevier B.V. All rights reserved.

Software tools and efficient algorithms for the feature detection, feature tracking, event localization, and visualization of large sets of atmospheric data

Data sets describing the state of the earth's atmosphere are of great importance in the atmospheric sciences. Over the last decades, the quality and sheer amount of the available data increased significantly, resulting in a rising demand for new tools capable of handling and analysing these large, multidimensional sets of atmospheric data. The interdisciplinary work presented in this thesis covers the development and the application of practical software tools and efficient algorithms from the field of computer science, aiming at the goal of enabling atmospheric scientists to analyse and to gain new insights from these large data sets. For this purpose, our tools combine novel techniques with well-established methods from different areas such as scientific visualization and data segmentation. In this thesis, three practical tools are presented. Two of these tools are software systems (Insight and IWAL) for different types of processing and interactive visualization of data, the third tool is an efficient algorithm for data segmentation implemented as part of Insight.Insight is a toolkit for the interactive, three-dimensional visualization and processing of large sets of atmospheric data, originally developed as a testing environment for the novel segmentation algorithm. It provides a dynamic system for combining at runtime data from different sources, a variety of different data processing algorithms, and several visualization techniques. Its modular architecture and flexible scripting support led to additional applications of the software, from which two examples are presented: the usage of Insight as a WMS (web map service) server, and the automatic production of a sequence of images for the visualization of cyclone simulations. The core application of Insight is the provision of the novel segmentation algorithm for the efficient detection and tracking of 3D features in large sets of atmospheric data, as well as for the precise localization of the occurring genesis, lysis, merging and splitting events. Data segmentation usually leads to a significant reduction of the size of the considered data. This enables a practical visualization of the data, statistical analyses of the features and their events, and the manual or automatic detection of interesting situations for subsequent detailed investigation. The concepts of the novel algorithm, its technical realization, and several extensions for avoiding under- and over-segmentation are discussed. As example applications, this thesis covers the setup and the results of the segmentation of upper-tropospheric jet streams and cyclones as full 3D objects. Finally, IWAL is presented, which is a web application for providing an easy interactive access to meteorological data visualizations, primarily aimed at students. As a web application, the needs to retrieve all input data sets and to install and handle complex visualization tools on a local machine are avoided. The main challenge in the provision of customizable visualizations to large numbers of simultaneous users was to find an acceptable trade-off between the available visualization options and the performance of the application. Besides the implementational details, benchmarks and the results of a user survey are presented.

VR Based Visualization and Exploration of Plant Biological Data

This paper investigates the use of virtual reality (VR) technologies to facilitate the analysis of plant biological data in distinctive steps in the application pipeline. Reconstructed three-dimensional biological models (primary polygonal models) transferred to a virtual environment support scientists' collaborative exploration of biological datasets so that they obtain accurate analysis results and uncover information hidden in the data. Examples of the use of virtual reality in practice are provided and a complementary user study was performed.

Visualization and quantification of air embolism structure by processing postmortem MSCT data.

Venous air embolism (VAE) is an often occurring forensic finding in cases of injury to the head and neck. Whenever found, it has to be appraised in its relation to the cause of death. While visualization and quantification is difficult at traditional autopsy, Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) offer a new potential in the diagnosis of VAE. This paper reports the findings of VAE in four cases of massive head injury examined postmortem by Multislice Computed Tomography (MSCT) prior to autopsy. MSCT data of the thorax were processed using 3D air structure reconstruction software to visualize air embolism within the vascular system. Quantification of VAE was done by multiplying air containing areas on axial 2D images by their reconstruction intervals and then by summarizing the air volumes. Excellent 3D visualization of the air within the vascular system was obtained in all cases, and the intravascular gas volume was quantified.

3D documentation and visualization of external injury findings by integration of simple photography in CT/MRI data sets (IprojeCT).

This study evaluated the feasibility of documenting patterned injury using three dimensions and true colour photography without complex 3D surface documentation methods. This method is based on a generated 3D surface model using radiologic slice images (CT) while the colour information is derived from photographs taken with commercially available cameras. The external patterned injuries were documented in 16 cases using digital photography as well as highly precise photogrammetry-supported 3D structured light scanning. The internal findings of these deceased were recorded using CT and MRI. For registration of the internal with the external data, two different types of radiographic markers were used and compared. The 3D surface model generated from CT slice images was linked with the photographs, and thereby digital true-colour 3D models of the patterned injuries could be created (Image projection onto CT/IprojeCT). In addition, these external models were merged with the models of the somatic interior. We demonstrated that 3D documentation and visualization of external injury findings by integration of digital photography in CT/MRI data sets is suitable for the 3D documentation of individual patterned injuries to a body. Nevertheless, this documentation method is not a substitution for photogrammetry and surface scanning, especially when the entire bodily surface is to be recorded in three dimensions including all external findings, and when precise data is required for comparing highly detailed injury features with the injury-inflicting tool.

Timely Reporting and Interactive Visualization of Animal Health and Slaughterhouse Surveillance Data in Switzerland

The reporting of outputs from health surveillance systems should be done in a near real-time and interactive manner in order to provide decision makers with powerful means to identify, assess, and manage health hazards as early and efficiently as possible. While this is currently rarely the case in veterinary public health surveillance, reporting tools do exist for the visual exploration and interactive interrogation of health data. In this work, we used tools freely available from the Google Maps and Charts library to develop a web application reporting health-related data derived from slaughterhouse surveillance and from a newly established web-based equine surveillance system in Switzerland. Both sets of tools allowed entry-level usage without or with minimal programing skills while being flexible enough to cater for more complex scenarios for users with greater programing skills. In particular, interfaces linking statistical softwares and Google tools provide additional analytical functionality (such as algorithms for the detection of unusually high case occurrences) for inclusion in the reporting process. We show that such powerful approaches could improve timely dissemination and communication of technical information to decision makers and other stakeholders and could foster the early-warning capacity of animal health surveillance systems.