scholarly journals The Personalized Cancer Network Explorer (PeCaX) as a visual analytics tool to support molecular tumor boards

2021 ◽  
Author(s):  
Mirjam Figaschewski ◽  
Bilge Sürün ◽  
Thorsten Tiede ◽  
Oliver Kohlbacher
Keyword(s):  
Biological Networks ◽  
Visual Analytics ◽  
Patient Specific ◽  
Visual Interpretation ◽  
Web Based ◽  
Specific Tumor ◽  
Tumor Boards ◽  
Personalized Oncology ◽  
Personalized Cancer ◽  
Cancer Network

Personalized oncology represents a shift in cancer treatment from conventional methods to target specific therapies where the decisions are made based on the patient specific tumor profile. Selection of the optimal therapy relies on a complex interdisciplinary analysis and interpretation of these variants by experts in molecular tumor boards. With up to hundreds of somatic variants identified in a tumor, this process requires visual analytics tools to guide and accelerate the annotation process. The Personal Cancer Network Explorer (PeCaX) is a visual analytics tool supporting the efficient annotation, navigation, and interpretation of somatic genomic variants through functional annotation, drug target annotation, and visual interpretation within the context of biological networks. Starting with somatic variants in a VCF file, PeCaX enables users to explore these variants through a web-based graphical user interface. The most protruding feature of PeCaX is the combination of clinical variant annotation and gene- drug networks with an interactive visualization. This reduces the time and effort the user needs to invest to get to a treatment suggestion and helps to generate new hypotheses. PeCaX is being provided as a platform-independent containerized software package for local or institution-wide deployment. PeCaX is available for download at https://github.com/KohlbacherLab/PeCaX-docker.

scholarly journals CancerTracer: a curated database for intrapatient tumor heterogeneity

2019 ◽  
Author(s):  
Chen Wang ◽  
Jian Yang ◽  
Hong Luo ◽  
Kun Wang ◽  
Yu Wang ◽  
...  
Keyword(s):  
Phylogenetic Trees ◽  
Predictive Biomarkers ◽  
Patient Specific ◽  
Intratumor Heterogeneity ◽  
Cancer Therapies ◽  
Specific Tumor ◽  
Multiple Biopsies ◽  
Evolutionary Trajectories ◽  
Personalized Cancer ◽  
Clonal Composition

Abstract Comprehensive genomic analyses of cancers have revealed substantial intrapatient molecular heterogeneities that may explain some instances of drug resistance and treatment failures. Examination of the clonal composition of an individual tumor and its evolution through disease progression and treatment may enable identification of precise therapeutic targets for drug design. Multi-region and single-cell sequencing are powerful tools that can be used to capture intratumor heterogeneity. Here, we present a database we’ve named CancerTracer (http://cailab.labshare.cn/cancertracer): a manually curated database designed to track and characterize the evolutionary trajectories of tumor growth in individual patients. We collected over 6000 tumor samples from 1548 patients corresponding to 45 different types of cancer. Patient-specific tumor phylogenetic trees were constructed based on somatic mutations or copy number alterations identified in multiple biopsies. Using the structured heterogeneity data, researchers can identify common driver events shared by all tumor regions, and the heterogeneous somatic events present in different regions of a tumor of interest. The database can also be used to investigate the phylogenetic relationships between primary and metastatic tumors. It is our hope that CancerTracer will significantly improve our understanding of the evolutionary histories of tumors, and may facilitate the identification of predictive biomarkers for personalized cancer therapies.

scholarly journals Web-Based Simulation Environment for Vehicular Electrical Networks

Energies ◽  
2021 ◽  
Vol 14 (19) ◽  
pp. 6087
Author(s):  
Xavier Dominguez ◽  
Paola Mantilla-Pérez ◽  
Nuria Gimenez ◽  
Islam El-Sayed ◽  
Manuel Alberto Díaz Díaz Millán ◽  
...  
Keyword(s):  
Visual Analytics ◽  
Distribution Systems ◽  
Software Tool ◽  
Automatic Generation ◽  
Electrical Networks ◽  
Web Based ◽  
Electrical Distribution Systems ◽  
Technical Requirements ◽  
Information Interaction

For the validation of vehicular Electrical Distribution Systems (EDS), engineers are currently required to analyze disperse information regarding technical requirements, standards and datasheets. Moreover, an enormous effort takes place to elaborate testing plans that are representative for most EDS possible configurations. These experiments are followed by laborious data analysis. To diminish this workload and the need for physical resources, this work reports a simulation platform that centralizes the tasks for testing different EDS configurations and assists the early detection of inadequacies in the design process. A specific procedure is provided to develop a software tool intended for this aim. Moreover, the described functionalities are exemplified considering as a case study the main wire harness from a commercial vehicle. A web-based architecture has been employed in alignment with the ongoing software development revolution and thus provides flexibility for both, developers and users. Due to its scalability, the proposed software scheme can be extended to other web-based simulation applications. Furthermore, the automatic generation of electrical layouts for EDS is addressed to favor an intuitive understanding of the network. To favor human–information interaction, utilized visual analytics strategies are also discussed. Finally, full simulation workflows are exposed to provide further insights on the deployment of this type of computer platforms.

scholarly journals E-scape: Interactive visualization of single cell phylogenetics and spatio-temporal evolution in cancer

2016 ◽  
Author(s):  
Maia A. Smith ◽  
Cydney Nielsen ◽  
Fong Chun Chan ◽  
Andrew McPherson ◽  
Andrew Roth ◽  
...  
Keyword(s):  
Single Cell ◽  
Visual Analytics ◽  
Domain Knowledge ◽  
Treatment Resistance ◽  
Cancer Evolution ◽  
Imaging Data ◽  
Web Based ◽  
Clinical Endpoints ◽  
Cell Experiment ◽  
Spatio Temporal

Inference of clonal dynamics and tumour evolution has fundamental importance in understanding the major clinical endpoints in cancer: development of treatment resistance, relapse and metastasis. DNA sequencing technology has made measuring clonal dynamics through mutation analysis accessible at scale, facilitating computational inference of informative patterns of interest. However, currently no tools allow for biomedical experts to meaningfully interact with the often complex and voluminous dataset to inject domain knowledge into the inference process. We developed an interactive, web-based visual analytics software suite called E-scape which supports dynamically linked, multi-faceted views of cancer evolution data. Developed using R and javascript d3.js libraries, the suite includes three tools: TimeScape and MapScape for visualizing population dynamics over time and space, respectively, and CellScape for visualizing evolution at single cell resolution. The tool suite integrates phylogenetic, clonal prevalence, mutation and imaging data to generate intuitive, dynamically linked views of data which update in real time as a function of user actions. The system supports visualization of both point mutation and copy number alterations, rendering how mutations distribute in clones in both bulk and single cell experiment data in multiple representations including phylogenies, heatmaps, growth trajectories, spatial distributions and mutation tables. E-scape is open source and is freely available to the community at large.

scholarly journals COLAEVA: Visual Analytics and Data Mining Web-Based Tool for Virtual Coaching of Older Adult Populations

Sensors ◽  
2021 ◽  
Vol 21 (23) ◽  
pp. 7991
Author(s):  
Jon Kerexeta Sarriegi ◽  
Andoni Beristain Iraola ◽  
Roberto Álvarez Sánchez ◽  
Manuel Graña ◽  
Kristin May Rebescher ◽  
...  
Keyword(s):  
Older Adults ◽  
Older Adult ◽  
Visual Analytics ◽  
Web Application ◽  
Adult Population ◽  
Research Area ◽  
Web Based ◽  
Virtual Coaching ◽  
Evolution Analysis ◽  
Older Adult Population

The global population is aging in an unprecedented manner and the challenges for improving the lives of older adults are currently both a strong priority in the political and healthcare arena. In this sense, preventive measures and telemedicine have the potential to play an important role in improving the number of healthy years older adults may experience and virtual coaching is a promising research area to support this process. This paper presents COLAEVA, an interactive web application for older adult population clustering and evolution analysis. Its objective is to support caregivers in the design, validation and refinement of coaching plans adapted to specific population groups. COLAEVA enables coaching caregivers to interactively group similar older adults based on preliminary assessment data, using AI features, and to evaluate the influence of coaching plans once the final assessment is carried out for a baseline comparison. To evaluate COLAEVA, a usability test was carried out with 9 test participants obtaining an average SUS score of 71.1. Moreover, COLAEVA is available online to use and explore.

Space-borne terrain deformation monitoring for large infrastructure projects

2021 ◽  
Author(s):  
Regula Frauenfelder ◽  
Malte Vöge ◽  
Sean E. Salazar ◽  
Carsten Hauser
Keyword(s):  
High Resolution ◽  
Geodetic Survey ◽  
Development Project ◽  
Deformation Monitoring ◽  
Cost Driver ◽  
Development Projects ◽  
Infrastructure Projects ◽  
Visual Interpretation ◽  
Web Based ◽  
Railway Line

<p>Ground settlement and associated deformation of existing infrastructure is a major risk in urban development projects. Project owners have a responsibility to document and manage settlement records before, during and after construction works. Traditionally, land surveying (e.g. leveling and total station) techniques have been the state-of-practice to provide settlement monitoring data. However, in big infrastructure projects, conventional geodetic data acquisition is a major cost driver. Modern space-borne radar interferometry (InSAR) provides the opportunity to drastically increase the number of monitored locations, while at the same time reducing expenses for traditional geodetic survey work. Furthermore, the method allows for highly effective monitoring during all phases of a project.</p><p>The application of InSAR technology is demonstrated for three large development projects near Oslo, the capital of Norway. Showcase examples include a new highway development project and two railway line upgrade projects. In two of the cases, InSAR monitoring was performed by exploiting very high resolution TerraSAR-X data (ca. 1.5 x 1.5 m spatial ground resolution), and in one case, using high resolution Radarsat-2 data (ca. 7 x 7 m spatial ground resolution). A combined area of 127 km<sup>2</sup> was monitored for all three projects, yielding a total of roughly 800,000 measurement points on the ground. Achieved measurement point density based on the TerraSAR-X data was around 37,000 points per km<sup>2</sup>, while density based on the Radarsat-2 data resulted in approximately 6,000 points per km<sup>2</sup> in built-up areas. Both data resolutions offer millimetric deformation precision, with surfaces of buildings and infrastructure providing the best signal reflection and phase coherence, resulting in high-quality results. In all cases, the interferometric time series analyses were communicated to the end users through a web-based map portal, enabling simple visual interpretation of the results, as well as integration with the settlement records of the project.</p>

scholarly journals Visual Evidence: Increasing Usability of Systematic Reviews in Health Systems Guidelines Development

2019 ◽  
Vol 10 (04) ◽  
pp. 743-750 ◽  
Author(s):  
Connor J. Smith ◽  
Rebecca M. Jungbauer ◽  
Annette M. Totten
Keyword(s):  
Systematic Review ◽  
Systematic Reviews ◽  
Visual Analytics ◽  
Dynamic Visualization ◽  
Static Structure ◽  
Web Based ◽  
Guidelines Development ◽  
Potential Benefits ◽  
Visualization Of Data ◽  
Aid Decision

Abstract Background Integration of evidence from systematic reviews is an essential step in the development of clinical guidelines. The current practice for reporting uses a static structure that does not allow for dynamic investigation. A need exists for an alternate reporting modality to facilitate dynamic visualization of results to match different end-users' queries. Objectives We developed a dynamic visualization of data from a systematic review using the commercial product Tableau and assessed its potential to permit customized inquiries. Methods Data were selected and extracted from a previously completed systematic review. The resulting dataset was then used to develop an interactive, web-based report designed for use by a guidelines development committee. Results A novel example of combining existing reporting standards for systematic review data and modern reporting tools was developed to investigate potential benefits of a dynamic report. Demonstrations of the report to clinicians sitting on previous and future guideline committees received positive feedback for its potential benefit in guidelines development. The report received a runner-up award during the design challenge at the 2018 Workshop on Visual Analytics in Health Care. Conclusion The use of interactive, accessible data may increase the use of systematic reviews and aid decision makers in developing evidence-based practice changes.

scholarly journals Adaptive Choropleth Mapper: An Open-Source Web-Based Tool for Synchronous Exploration of Multiple Variables at Multiple Spatial Extents

2019 ◽  
Vol 8 (11) ◽  
pp. 509 ◽  
Author(s):  
Han ◽  
Rey ◽  
Knaap ◽  
Kang ◽  
Wolf
Keyword(s):  
Open Source ◽  
Spatial Data ◽  
Visual Analytics ◽  
Spatial Data Analysis ◽  
Dynamic Visualization ◽  
Web Based ◽  
Choropleth Mapping ◽  
Choropleth Maps ◽  
Multiple Variables ◽  
Choropleth Map

Choropleth mapping is an essential visualization technique for exploratory spatial data analysis. Visualizing multiple choropleth maps is a technique that spatial analysts use to reveal spatiotemporal patterns of one variable or to compare the geographical distributions of multiple variables. Critical features for effective exploration of multiple choropleth maps are (1) automated computation of the same class intervals for shading different choropleth maps, (2) dynamic visualization of local variation in a variable, and (3) linking for synchronous exploration of multiple choropleth maps. Since the 1990s, these features have been developed and are now included in many commercial geographic information system (GIS) software packages. However, many choropleth mapping tools include only one or two of the three features described above. On the other hand, freely available mapping tools that support side-by-side multiple choropleth map visualizations are usually desktop software only. As a result, most existing tools supporting multiple choropleth-map visualizations cannot be easily integrated with Web-based and open-source data visualization libraries, which have become mainstream in visual analytics and geovisualization. To fill this gap, we introduce an open-source Web-based choropleth mapping tool called the Adaptive Choropleth Mapper (ACM), which combines the three critical features for flexible choropleth mapping.

scholarly journals ctDNA monitoring using patient-specific sequencing and integration of variant reads

2020 ◽  
Vol 12 (548) ◽  
pp. eaaz8084 ◽  
Author(s):  
Jonathan C. M. Wan ◽  
Katrin Heider ◽  
Davina Gale ◽  
Suzanne Murphy ◽  
Eyal Fisher ◽  
...  
Keyword(s):  
Residual Disease ◽  
Early Stage ◽  
Area Under The Curve ◽  
Patient Specific ◽  
Sequencing Data ◽  
Liquid Biopsies ◽  
Whole Exome ◽  
Mutation Burden ◽  
Median Area ◽  
Personalized Cancer

Circulating tumor-derived DNA (ctDNA) can be used to monitor cancer dynamics noninvasively. Detection of ctDNA can be challenging in patients with low-volume or residual disease, where plasma contains very few tumor-derived DNA fragments. We show that sensitivity for ctDNA detection in plasma can be improved by analyzing hundreds to thousands of mutations that are first identified by tumor genotyping. We describe the INtegration of VAriant Reads (INVAR) pipeline, which combines custom error-suppression methods and signal-enrichment approaches based on biological features of ctDNA. With this approach, the detection limit in each sample can be estimated independently based on the number of informative reads sequenced across multiple patient-specific loci. We applied INVAR to custom hybrid-capture sequencing data from 176 plasma samples from 105 patients with melanoma, lung, renal, glioma, and breast cancer across both early and advanced disease. By integrating signal across a median of >105 informative reads, ctDNA was routinely quantified to 1 mutant molecule per 100,000, and in some cases with high tumor mutation burden and/or plasma input material, to parts per million. This resulted in median area under the curve (AUC) values of 0.98 in advanced cancers and 0.80 in early-stage and challenging settings for ctDNA detection. We generalized this method to whole-exome and whole-genome sequencing, showing that INVAR may be applied without requiring personalized sequencing panels so long as a tumor mutation list is available. As tumor sequencing becomes increasingly performed, such methods for personalized cancer monitoring may enhance the sensitivity of cancer liquid biopsies.

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