Visual Analytics Tool Research Articles

A Visual Analytics Approach for Comparing Cohorts in Single-Voxel Magnetic Resonance Spectroscopy Data.

Single-voxel proton magnetic resonance spectroscopy (1H-MRS) is a non-invasive in-vivo technology to measure metabolic concentrations in selected regions of interest in a tissue, e.g., the brain. 1H-MRS generates spectra of signals with different frequencies and specific intensities which can be assigned to respective metabolites in the investigated tissue and quantified. In studies designed to detect biomarkers of a specific disorder or dysfunction, the overall goal is not just to analyze a single 1H-MRS data set, but to compare patient cohorts against healthy controls. We propose a visual analytics tool for the comparative analyses of cohorts, i.e., sets of data sets. Each data set can be regarded as a multivariate data sample, in which each variable represents the concentration of a metabolite. While a standard workflow for comparative analyses of two cohorts is routinely deployed by analyzing metabolites individually, our tool allows for comparative cohort analysis in a multivariate setting. Our top-down analysis strategy uses multidimensional data visualization methods combined with statistical plots and statistical analyses. We document and evaluate the effectiveness of our approach for the interactive analysis of metabolite concentrations in three brain regions for a comparative study of an alcohol-dependent patient cohort and a healthy control group.

SPortfolio: Stratified Visual Analysis of Stock Portfolios.

Quantitative Investment, built on the solid foundation of robust financial theories, is at the center stage in investment industry today. The essence of quantitative investment is the multi-factor model, which explains the relationship between the risk and return of equities. However, the multi-factor model generates enormous quantities of factor data, through which even experienced portfolio managers find it difficult to navigate. This has led to portfolio analysis and factor research being limited by a lack of intuitive visual analytics tools. Previous portfolio visualization systems have mainly focused on the relationship between the portfolio return and stock holdings, which is insufficient for making actionable insights or understanding market trends. In this paper, we present s Portfolio, which, to the best of our knowledge, is the first visualization that attempts to explore the factor investment area. In particular, sPortfolio provides a holistic overview of the factor data and aims to facilitate the analysis at three different levels: a Risk-Factor level, for a general market situation analysis; a Multiple-Portfolio level, for understanding the portfolio strategies; and a Single-Portfolio level, for investigating detailed operations. The system's effectiveness and usability are demonstrated through three case studies. The system has passed its pilot study and is soon to be deployed in industry.

Enhancing theWeb WithAdvanced Engineering

Enhancing theWeb WithAdvanced Engineering

An Approach to Identify Delivery of Palliative Radiation Therapy Using Health Care Claims Data: A Proof-of-Concept Application of a Visual Analytics Tool.

There is limited information on the use of data visualization tools for health services research applications. We provide a proof-of-concept application that focuses on claims-based measures of palliative radiation therapy. We investigate whether a guided, data-driven investigation contributes information for subsequent statistical analysis and algorithm development. This retrospective cohort study used linked registry and claims data on men who were diagnosed with stage IV M0 or stage IV M1b prostate cancer between 2005 and 2009, with associated claims from 2005 through 2010, and receiving radiation therapy. Preprocessing of data was accomplished by using EventFlow software to investigate longitudinal patterns in claims for radiation therapy in the 13 months after cancer diagnosis. Guided by results from EventFlow, we developed descriptive statistics to investigate the length of radiation therapy, use of bone metastasis coding, and mortality between M1b and M0 patients. A total of 1,151 patients met the inclusion criteria. Taking advantage of the novel aggregation capability of EventFlow, we observed differences in the length of radiation therapy and the use of bone metastasis coding between men with (M1b) and without (M0) a diagnosis of bone metastasis. Seventy-nine percent of M1b patients received radiation for a duration ≤ 4 weeks, which suggested palliative radiation (to the bone). Seventy-six percent of M0 patients received radiation for ≥ 6 weeks, which suggested radiation to the prostate. Mortality was higher among those who received a shorter duration of radiation therapy compared with those who received a longer duration of therapy. Use of EventFlow, followed by statistical analysis of the linked registry and claims data, identified useful components of a claims-based measure of radiation to the bone.

Data Access and Interactive Visualization of Whole Genome Sequence of Sickle Cell Patients within the St. Jude Cloud

With the increase in availability of high depth whole genome sequencing (WGS) data of individuals with sickle cell disease (SCD), easy access to the raw sequencing data remains an issue due to technical and regulatory challenges. A compliant system that can provide facile data access would accelerate scientific discovery of genetic variants associated with clinical phenotypes. Cloud storage and computing provide an ultimate solution to this data access, which we have shown through the St. Jude Cloud (https://stjude.cloud) where over 5000 whole genome sequences for pediatric cancer patients are being shared in collaboration with DNANexus and Microsoft. Here we expand the St. Jude Cloud to sickle cell disease data through the Sickle Genome Project (SGP) Data Portal (https://pecan.stjude.org/permalink/sgp) to allow instantaneous raw data access (following data access committee approval), as well as visualization of genotype calls at individual level in a novel genome. The SGP WGS data was generated from 871 patients from St. Jude Children's Research Hospital (St. Jude) through the Sickle Cell Clinical Research and Intervention Program (SCCRIP, Pediatr Blood Cancer. 2018 May 24: e27228) and from Baylor College of Medicine (BCM). All study participants provided informed consent for genomic study and data sharing on IRB-approved research protocols.The SGP data portal will have multi-tiered access. All users will have access to a general heat map view which shows anonymized patient clinical values (e.g., fetal hemoglobin (HbF), mean corpuscular volume (MCV), hemoglobin concentration (Hb)) and relevant SCD modifying variants (e.g., Beta-globin locus, MYB, BCL11A, HBA). The GenomePaint bowser allows for viewing coding and noncoding variants. Displayed with each variant will be a visual indication of the median fetal hemoglobin values for patients homozygous for the reference allele, heterozygous, or homozygous for the alternative allele. The browser also displays erythroid specific DNA-accessibility and epigenetic marks and indicates variant that may disrupt erythroid specific transcription factor binding sites (GATA1 and BCL11A). For anonymization purposes, within the genome browser and heat map views, clinical values and the patients age will be binned into ranges when displayed as single or low count values. Lastly, the ProteinPaint tool (Zhang and Zhou, Nature Gen, Dec 29, 2015) will enable visualization and filtering of variants with reference to protein domain and amino acid sequence.To access processed data such as BAM and VCF files for downstream analyses, a user will be required to apply for access which will be adjudicated by a data access committee. Verified researchers will be granted access to clinical data in a manner consistent with the protocol specific informed consent documentation and protocol under which the sequencing was performed. This may include coded clinical and demographic data when specified by the research protocol and informed consentThe SGP data set will be one of the first WGS datasets from primarily African American Sickle cell patients to be made available to clinicians and researchers worldwide. In addition, no SCD-centric data portal exists that contains controlled access to data and provides graphical tools for visual analysis. The combination of the visual tools and ability to download tools provides the scientific community an invaluable resource for studying sickle cell disease. DisclosuresEstepp:Daiichi Sankyo: Consultancy; NHLBI: Research Funding; Global Blood Therapeutics: Consultancy, Research Funding; ASH Scholar: Research Funding. Hankins:Global Blood Therapeutics: Research Funding; bluebird bio: Consultancy; NCQA: Consultancy; Novartis: Research Funding.

VIOLA-A Multi-Purpose and Web-Based Visualization Tool for Neuronal-Network Simulation Output.

Neuronal network models and corresponding computer simulations are invaluable tools to aid the interpretation of the relationship between neuron properties, connectivity, and measured activity in cortical tissue. Spatiotemporal patterns of activity propagating across the cortical surface as observed experimentally can for example be described by neuronal network models with layered geometry and distance-dependent connectivity. In order to cover the surface area captured by today's experimental techniques and to achieve sufficient self-consistency, such models contain millions of nerve cells. The interpretation of the resulting stream of multi-modal and multi-dimensional simulation data calls for integrating interactive visualization steps into existing simulation-analysis workflows. Here, we present a set of interactive visualization concepts called views for the visual analysis of activity data in topological network models, and a corresponding reference implementation VIOLA (VIsualization Of Layer Activity). The software is a lightweight, open-source, web-based, and platform-independent application combining and adapting modern interactive visualization paradigms, such as coordinated multiple views, for massively parallel neurophysiological data. For a use-case demonstration we consider spiking activity data of a two-population, layered point-neuron network model incorporating distance-dependent connectivity subject to a spatially confined excitation originating from an external population. With the multiple coordinated views, an explorative and qualitative assessment of the spatiotemporal features of neuronal activity can be performed upfront of a detailed quantitative data analysis of specific aspects of the data. Interactive multi-view analysis therefore assists existing data analysis workflows. Furthermore, ongoing efforts including the European Human Brain Project aim at providing online user portals for integrated model development, simulation, analysis, and provenance tracking, wherein interactive visual analysis tools are one component. Browser-compatible, web-technology based solutions are therefore required. Within this scope, with VIOLA we provide a first prototype.

Plasmid ATLAS: plasmid visual analytics and identification in high-throughput sequencing data.

Plasmid ATLAS (pATLAS, http://www.patlas.site) provides an easy-to-use web accessible database with visual analytics tools to explore the relationships of plasmids available in NCBI’s RefSeq database. pATLAS has two main goals: (i) to provide an easy way to search for plasmids deposited in NCBI RefSeq and their associated metadata; (ii) to visualize the relationships of plasmids in a graph, allowing the exploration of plasmid evolution. pATLAS allows searching by plasmid name, bacterial host taxa, antibiotic resistance and virulence genes, plasmid families, and by sequence length and similarity. pATLAS is also able to represent in the plasmid network, plasmid sets identified by external pipelines using mapping, mash screen or assembly from high-throughput sequencing data. By representing the identified hits within the network of relationships between plasmids, allowing the possibility of removing redundant results, and by taking advantage of the browsing capabilities of pATLAS, users can more easily interpret the pipelines’ results. All these analyses can be saved to a JSON file for sharing and future re-evaluation. Furthermore, by offering a REST-API, the pATLAS database and network display are easily accessible by other interfaces or pipelines.

Visual Analytics Need, Process, Scope, Tools and Techniques and Challenges

International Journal of Computer Sciences and Engineering (A UGC Approved and indexed with DOI, ICI and Approved, DPI Digital Library) is one of the leading and growing open access, peer-reviewed, monthly, and scientific research journal for scientists, engineers, research scholars, and academicians, which gains a foothold in Asia and opens to the world, aims to publish original, theoretical and practical advances in Computer Science,Information Technology, Engineering (Software, Mechanical, Civil, Electronics & Electrical), and all interdisciplinary streams of Computing Sciences. It intends to disseminate original, scientific, theoretical or applied research in the field of Computer Sciences and allied fields. It provides a platform for publishing results and research with a strong empirical component. It aims to bridge the significant gap between research and practice by promoting the publication of original, novel, industry-relevant research.

Visualizing Physical Activity Patterns among Community-Dwelling Older Adults: A Pilot Study.

Regular physical activity relates to physical and mental functioning in older people, and promoting physical activity has the potential to substantially reduce functional decline and improve well-being. Despite this, investigations of the physical activity quotient through participation in functional activities in everyday life have traditionally gained limited focus among older populations compared to leisure-time physical activity and exercise. Considering the accumulated evidence of the health benefits of low-intensity physical activity, exploring and measuring such activities in this population is highly relevant. The aim of this study was to visualize and describe older people’s physical activity patterns in daily life using a time-geographic approach in combination with the estimation of metabolic equivalents (METS). To exemplify the new method, a sample of nine retired men (65–82 years old, mean age 76.4 ± 5.8) with no homecare services from the municipality was recruited. In order to enable a visual analysis of the physical activity patterns in daily life, we developed the VISUAL-PA software, which is a visual analysis tool that includes METS to account for intensity and enables the analysis of distinct types and domains of physical activity. The VISUAL-PA software creates graphic outputs of physical activity patterns that enable the identification, visualization, and analysis of distinct types and intensities of physical activity in addition to sedentary behavior. The use of VISUAL-PA can contribute to a broader understanding of the complexity in physical activity patterns among older adults in terms of dimensions such as activity patterns and habits, domains, and intensity level. To strengthen the public health strategies that promote health and an active lifestyle, additional knowledge about physical activity patterns is necessary. Moreover, the visualization of physical activity can enable reflections on and awareness of activity habits and preferences, and thus facilitate behavior changes in older individuals.

SEQ2SEQ-VIS : A Visual Debugging Tool for Sequence-to-Sequence Models.

Neural sequence-to-sequence models have proven to be accurate and robust for many sequence prediction tasks, and have become the standard approach for automatic translation of text. The models work with a five-stage blackbox pipeline that begins with encoding a source sequence to a vector space and then decoding out to a new target sequence. This process is now standard, but like many deep learning methods remains quite difficult to understand or debug. In this work, we present a visual analysis tool that allows interaction and "what if"-style exploration of trained sequence-to-sequence models through each stage of the translation process. The aim is to identify which patterns have been learned, to detect model errors, and to probe the model with counterfactual scenario. We demonstrate the utility of our tool through several real-world sequence-to-sequence use cases on large-scale models.

Topic-Based Exploration and Embedded Visualizations for Research Idea Generation.

This work analyzes sensemaking frameworks and experiments with an iteratively designed visual analysis tool to identify design implications for facilitating research idea generation using visualizations. Our tool, ThoughtFlow, structures and visualizes literature collections using topic models to bridge the information gap between core activities during research ideation. To help users stay focused on a topic while discovering relevant documents, we designed and analyzed usage patterns for two types of embedded visualization that help determine document relevance while minimizing distraction. We analyzed how research ideation outcomes and processes differ when using ThoughtFlow and conventional search engines by augmenting insight-based evaluation with concept-map analysis. Our results suggest that operations afforded by topic models match well with later ideation stages when coherent topics have emerged, but not with early stages when users are still relying heavily on individual keywords to gather background knowledge. We also present qualitative evidence that citation sparklines encourage more exploration of recommended references, and that a preference for paper thumbnails may depend on the consistency between the evidence and the current mental frame.

Intrinsic protein geometry with application to non-proline cis peptide planes

The shape of a protein can be modeled by the \(\hbox {C}^{\alpha }\) atoms of its backbone, the mathematical description employing the notion of extrinsic geometry of a discrete piecewise linear chain. We advance differential geometry of a natively framed discrete chain to argue the existence of two additional, independent and intrinsic geometric structures, provided by the peptide planes and side chains, respectively. We develop our general methodology within a case study: analysis of the intrinsic geometry of atoms that are located around a non-proline cis peptide plane. We show that the native peptide plane framing allows for revealing of atomic positions anomalies. That way, we identify a number of entries that display such anomalies around their non-proline cis peptide planes within the ultrahigh-resolution structures in PDB. We propose that our approach can be extended into a visual analysis and refinement tool that is applicable even when resolution is limited or data is incomplete, for example when there are atoms missing in an experimental construct.

RegressionExplorer: Interactive Exploration of Logistic Regression Models with Subgroup Analysis.

We present RegressionExplorer, a Visual Analytics tool for the interactive exploration of logistic regression models. Our application domain is Clinical Biostatistics, where models are derived from patient data with the aim to obtain clinically meaningful insights and consequences. Development and interpretation of a proper model requires domain expertise and insight into model characteristics. Because of time constraints, often a limited number of candidate models is evaluated. RegressionExplorer enables experts to quickly generate, evaluate, and compare many different models, taking the workflow for model development as starting point. Global patterns in parameter values of candidate models can be explored effectively. In addition, experts are enabled to compare candidate models across multiple subpopulations. The insights obtained can be used to formulate new hypotheses or to steer model development. The effectiveness of the tool is demonstrated for two uses cases: prediction of a cardiac conduction disorder in patients after receiving a heart valve implant and prediction of hypernatremia in critically ill patients.

Use of NoSQL Database and Visualization Techniques to Analyze Massive Scholarly Article Data from Journals

Visualization of massive data is a challenging endeavour. Extracting data and providing graphical representations can aid in its effective utilization in terms of interpretation and knowledge discovery. Publishing research articles has become a way of life for academicians. The scholarly publications can shape-up the professional growth of authors and also expand the research and technological growth of a country, continent and other demographic regions. Scholarly articles have grown in gigantic numbers that are published in different domains by various journals. Information related to articles, authors, their affiliations, number of citations, country, publisher, references and other information is like a gold mine for statisticians and data analysts. This data when used skilfully, via visual analysis tool, can provide valuable understanding and can aid in deeper exposition for researchers working in domains like scientometrics and bibliometrics. Since the data is not readily available, we used Google scholar, a comprehensive and free repository of scholarly articles, as data source for our study. Data was scraped from Google scholar and stored as a graph and later visualized in the form of nodes and its relationships, which offered discerning and concealed information of growing impact of articles, journals and authors in their domains. Not only this, evident domain shift of an author, various research domains spread for an author, predicting emerging domain and subdomains, detecting cartel behaviour at Journal and author-level was also depicted by graphical analysis. Neo4j graph database was used in the background to help store the data in structured manner.

Advanced analytics for criminological research of terrorist attacks

This paper describes a multilingual text summarization and visual analytics tool that provides a searchable database of over 5,000 different online sources, from news portals to social media links and online documents, related to 10 case studies of terrorism for the period 2013 – 2017.

Recent advances in international marine econometrics

Purpose Since 2000, China, along with the USA, UK, France, Japan and many other developed countries have drawn up new blueprints for the development of a marine economy. At present, international marine economics research has entered into a new period of development, and the research methods of ocean econometrics are becoming more complex and mature. The purpose of this paper is to review the progress of international marine econometrics research and gives the development direction of marine econometrics. Design/methodology/approach The Web of Science core collection database was utilized, harvesting data from 1996 to May 2018, measuring the marine economy research from 1,489 articles as its sample, using CiteSpace visualization analysis tools. Findings Mapping the knowledge map from annual international marine economic metrology, literature identification, keywords, involving disciplines and related journals, countries (regions) and research and analyzing the research status of reveals the research frontiers of international marine economy measurement (learning) by using CiteSpace. Originality/value The conceptions and characteristics of marine econometrics are defined and analyzed, and the theoretical method of marine econometrics is sorted out. Mapping the knowledge diagram of marine econometrics and discussing the research status of international marine economics, and clarifying the existing problems, future opportunities and challenges of international marine econometrics research.

RetainVis: Visual Analytics with Interpretable and Interactive Recurrent Neural Networks on Electronic Medical Records.

We have recently seen many successful applications of recurrent neural networks (RNNs) on electronic medical records (EMRs), which contain histories of patients' diagnoses, medications, and other various events, in order to predict the current and future states of patients. Despite the strong performance of RNNs, it is often challenging for users to understand why the model makes a particular prediction. Such black-box nature of RNNs can impede its wide adoption in clinical practice. Furthermore, we have no established methods to interactively leverage users' domain expertise and prior knowledge as inputs for steering the model. Therefore, our design study aims to provide a visual analytics solution to increase interpretability and interactivity of RNNs via a joint effort of medical experts, artificial intelligence scientists, and visual analytics researchers. Following the iterative design process between the experts, we design, implement, and evaluate a visual analytics tool called RetainVis, which couples a newly improved, interpretable, and interactive RNN-based model called RetainEX and visualizations for users' exploration of EMR data in the context of prediction tasks. Our study shows the effective use of RetainVis for gaining insights into how individual medical codes contribute to making risk predictions, using EMRs of patients with heart failure and cataract symptoms. Our study also demonstrates how we made substantial changes to the state-of-the-art RNN model called RETAIN in order to make use of temporal information and increase interactivity. This study will provide a useful guideline for researchers that aim to design an interpretable and interactive visual analytics tool for RNNs.

An Interactive Framework for Visualization of Weather Forecast Ensembles.

Numerical Weather Prediction (NWP) ensembles are commonly used to assess the uncertainty and confidence in weather forecasts. Spaghetti plots are conventional tools for meteorologists to directly examine the uncertainty exhibited by ensembles, where they simultaneously visualize isocontours of all ensemble members. To avoid visual clutter in practical usages, one needs to select a small number of informative isovalues for visual analysis. Moreover, due to the complex topology and variation of ensemble isocontours, it is often a challenging task to interpret the spaghetti plot for even a single isovalue in large ensembles. In this paper, we propose an interactive framework for uncertainty visualization of weather forecast ensembles that significantly improves and expands the utility of spaghetti plots in ensemble analysis. Complementary to state-of-the-art methods, our approach provides a complete framework for visual exploration of ensemble isocontours, including isovalue selection, interactive isocontour variability exploration, and interactive sub-region selection and re-analysis. Our framework is built upon the high-density clustering paradigm, where the mode structure of the density function is represented as a hierarchy of nested subsets of the data. We generalize the high-density clustering for isocontours and propose a bandwidth selection method for estimating the density function of ensemble isocontours. We present novel visualizations based on high-density clustering results, called the mode plot and the simplified spaghetti plot. The proposed mode plot visually encodes the structure provided by the high-density clustering result and summarizes the distribution of ensemble isocontours. It also enables the selection of subsets of interesting isocontours, which are interactively highlighted in a linked spaghetti plot for providing spatial context. To provide an interpretable overview of the positional variability of isocontours, our system allows for selection of informative isovalues from the simplified spaghetti plot. Due to the spatial variability of ensemble isocontours, the system allows for interactive selection and focus on sub-regions for local uncertainty and clustering re-analysis. We examine a number of ensemble datasets to establish the utility of our approach and discuss its advantages over state-of-the-art visual analysis tools for ensemble data.

An Uncertainty Visual Analytics Framework for fMRI Functional Connectivity.

Analysis and interpretation of functional magnetic resonance imaging (fMRI) has been used to characterise many neuronal diseases, such as schizophrenia, bipolar disorder and Alzheimer's disease. Functional connectivity networks (FCNs) are widely used because they greatly reduce the amount of data that needs to be interpreted and they provide a common network structure that can be directly compared. However, FCNs contain a range of data uncertainties stemming from inherent limitations, e.g. during acquisition, as well as the loss of voxel-level data, and the use of thresholding in data abstraction. Additionally, human uncertainties arise during interpretation due to the complexity in understanding the data. While existing FCN visual analytics tools have begun to mitigate the human ambiguities, reducing the impact of data limitations is an open problem. In this paper, we propose a novel visual analytics framework with three linked, purpose-designed components to evoke deeper interpretation of the fMRI data: (i) an enhanced FCN abstraction; (ii) a temporal signal viewer; and (iii) the anatomical context. Each component has been specifically designed with novel visual cues and interaction to expose the impact of uncertainties on the data. We augment this with two methods designed for comparing subjects, by using a small multiples and a marker approach. We demonstrate the enhancements enabled by our framework on three case studies of common research scenarios, using clinical schizophrenia data, which highlight the value in interpreting fMRI FCN data with an awareness of the uncertainties. Finally, we discuss our framework in the context of fMRI visual analytics and the extensibility of our approach.

Copycat Layout: Network layout alignment via Cytoscape Automation.

The copycatLayout app is a network-based visual differential analysis tool that improves upon the existing layoutSaver app and is delivered pre-installed with Cytoscape, beginning with v3.6.0. LayoutSaver cloned a network layout by mapping node locations from one network to another based on node attribute values, but failed to clone view scale and location, and provided no means of identifying which nodes were successfully mapped between networks. Copycat addresses these issues and provides additional layout options. With the advent of Cytoscape Automation (packaged in Cytoscape v3.6.0), researchers can utilize the Copycat layout and its output in workflows written in their language of choice by using only a few simple REST calls. Copycat enables researchers to visually compare groups of homologous genes, generate network comparison images for publications, and quickly identify differences between similar networks at a glance without leaving their script. With a few extra REST calls, scripts can discover nodes present in one network but not in the other, which can feed into more complex analyses (e.g., modifying mismatched nodes based on new data, then re-running the layout to highlight additional network changes).