scholarly journals GLAD: GLycan Array Dashboard, a visual analytics tool for glycan microarrays

2019 ◽  
Vol 35 (18) ◽  
pp. 3536-3537 ◽  
Author(s):  
Akul Y Mehta ◽  
Richard D Cummings
Keyword(s):  
Data Mining ◽  
Microarray Data ◽  
Visual Analytics ◽  
Supplementary Information ◽  
Glycan Array ◽  
Visual Data Mining ◽  
Array Data ◽  
Glycan Microarray ◽  
Multiple Data ◽  
Data Files

Abstract Motivation Traditional glycan microarray data is typically presented as excel files with limited visualization and interactivity. Thus, comparisons and analysis of glycan array data have been difficult, and there is need for a tool to facilitate data mining of glycan array data. Results GLAD (GLycan Array Dashboard) is a web-based tool to visualize, analyze, present and mine glycan microarray data. GLAD allows users to input multiple data files to create comparisons. GLAD extends the capability of the microarray data to produce more comparative visualizations in the form of grouped bar charts, heatmaps, calendar heatmaps, force graphs and correlation maps in order to analyze broad sets of samples. Additionally, it allows users to filter, sort and normalize the data and view glycan structures in an interactive manner, to facilitate faster visual data mining. Availability and implementation GLAD is freely available for use on the Web at https://glycotoolkit.com/Tools/GLAD/ with all major modern browsers (Edge, Firefox, Chrome, Safari). Supplementary information Full documentation and video tutorials for GLAD can be found on https://glycotoolkit.com/GLAD.

scholarly journals Graphical data mining of cancer mechanisms with SEMA

2019 ◽  
Vol 35 (21) ◽  
pp. 4413-4418 ◽  
Author(s):  
Mustafa Solmaz ◽  
Adam Lane ◽  
Bilal Gonen ◽  
Ogulsheker Akmamedova ◽  
Mehmet H Gunes ◽  
...  
Keyword(s):  
Data Mining ◽  
Web Application ◽  
Cell Cycle Progression ◽  
Cancer Genomics ◽  
Supplementary Information ◽  
Visual Data Mining ◽  
Source Codes ◽  
Molecular Alterations ◽  
Regulation Of Cell Cycle ◽  
The Web

Abstract Motivation An important goal of cancer genomics initiatives is to provide the research community with the resources for the unbiased query of cancer mechanisms. Several excellent web platforms have been developed to enable the visual analyses of molecular alterations in cancers from these datasets. However, there are few tools to allow the researchers to mine these resources for mechanisms of cancer processes and their functional interactions in an intuitive unbiased manner. Results To address this need, we developed SEMA, a web platform for building and testing of models of cancer mechanisms from large multidimensional cancer genomics datasets. Unlike the existing tools for the analyses and query of these resources, SEMA is explicitly designed to enable exploratory and confirmatory analyses of complex cancer mechanisms through a suite of intuitive visual and statistical functionalities. Here, we present a case study of the functional mechanisms of TP53-mediated tumor suppression in various cancers, using SEMA, and identify its role in the regulation of cell cycle progression, DNA repair and signal transduction in different cancers. SEMA is a first-in-its-class web application designed to allow visual data mining and hypothesis testing from the multidimensional cancer datasets. The web application, an extensive tutorial and several video screencasts with case studies are freely available for academic use at https://sema.research.cchmc.org/. Availability and implementation SEMA is freely available at https://sema.research.cchmc.org. The web site also contains a detailed Tutorial (also in Supplementary Information), and a link to the YouTube channel for video screencasts of analyses, including the analyses presented here. The Shiny and JavaScript source codes have been deposited to GitHub: https://github.com/msolmazm/sema. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals GlyMDB: Glycan Microarray Database and analysis toolset

2019 ◽  
Vol 36 (8) ◽  
pp. 2438-2442 ◽  
Author(s):  
Yiwei Cao ◽  
Sang-Jun Park ◽  
Akul Y Mehta ◽  
Richard D Cummings ◽  
Wonpil Im
Keyword(s):  
Fluorescence Intensity ◽  
Microarray Data ◽  
Motif Discovery ◽  
Binding Proteins ◽  
Biological Activities ◽  
Supplementary Information ◽  
Binding Motif ◽  
Structural Determinants ◽  
Glycan Microarray ◽  
Microarray Database

Abstract Motivation Glycan microarrays are capable of illuminating the interactions of glycan-binding proteins (GBPs) against hundreds of defined glycan structures, and have revolutionized the investigations of protein–carbohydrate interactions underlying numerous critical biological activities. However, it is difficult to interpret microarray data and identify structural determinants promoting glycan binding to glycan-binding proteins due to the ambiguity in microarray fluorescence intensity and complexity in branched glycan structures. To facilitate analysis of glycan microarray data alongside protein structure, we have built the Glycan Microarray Database (GlyMDB), a web-based resource including a searchable database of glycan microarray samples and a toolset for data/structure analysis. Results The current GlyMDB provides data visualization and glycan-binding motif discovery for 5203 glycan microarray samples collected from the Consortium for Functional Glycomics. The unique feature of GlyMDB is to link microarray data to PDB structures. The GlyMDB provides different options for database query, and allows users to upload their microarray data for analysis. After search or upload is complete, users can choose the criterion for binder versus non-binder classification. They can view the signal intensity graph including the binder/non-binder threshold followed by a list of glycan-binding motifs. One can also compare the fluorescence intensity data from two different microarray samples. A protein sequence-based search is performed using BLAST to match microarray data with all available PDB structures containing glycans. The glycan ligand information is displayed, and links are provided for structural visualization and redirection to other modules in GlycanStructure.ORG for further investigation of glycan-binding sites and glycan structures. Availability and implementation http://www.glycanstructure.org/glymdb. Supplementary information Supplementary data are available at Bioinformatics online.

Data Visualization Indicator Disease (Malaria, Dengue Fever, and Measles) in The Year 2012-2015

2017 ◽  
Vol 4 (2) ◽  
pp. 87-93
Author(s):  
Immanuel Luigi Da Gusta ◽  
Johan Setiawan
Keyword(s):  
Data Mining ◽  
Human Resources ◽  
Data Visualization ◽  
Health Sector ◽  
Medical Personnel ◽  
Database Systems ◽  
Process Time ◽  
Acceptance Test ◽  
Visual Data Mining ◽  
The Government

The aim of this paper are: to create a data visualization that can assist the Government in evaluating the return on the development of health facilities in the region and province area in term of human resources for medical personnel, to help community knowing the amount of distribution of hospitals with medical personnel in the regional area and to map disease indicator in Indonesia. The issue of tackling health is still a major problem that is not resolved by the Government of Indonesia. There are three big things that become problems in the health sector in Indonesia: infrastructure has not been evenly distributed and less adequate, the lack of human resources professional health workforce, there is still a high number of deaths in the outbreak of infectious diseases. Data for the research are taken from BPS, in total 10,600 records after the Extract, Transform and Loading process. Time needed to convert several publications from PDF, to convert to CSV and then to MS Excel 3 weeks. The method used is Eight-step Data Visualization and Data Mining methodology. Tableau is chosen as a tool to create the data visualization because it can combine each dasboard inside a story interactive, easier for the user to analyze the data. The result is a story with 3 dashboards that can fulfill the requirement from BPS staff and has been tested with a satisfied result in the UAT (User Acceptance Test). Index Terms—Dashboard, data visualization, disease, malaria, Tableau REFERENCES [1] S. Arianto, Understanding of learning and others, 2008. [2] Rainer; Turban, Introduction to Information Systems, Danvers: John Wiley & Sons, Inc, 2007. [3] V. Friedman, Data Visualization Infographics, Monday Inspirition, 2008. [4] D. A. Keim, "Information Visualization and Visual Data Mining," IEEE Transactions on Visualization and Computer Graphics 8.1, pp. 1-8, 2002. [5] Connolly and Begg, Database Systems, Boston: Pearson Education, Inc, 2010. [6] E. Hariyanti, "Pengembangan Metodologi Pembangunan Information Dashboard Untuk Monitoring kinerja Organisasi," Konferensi dan Temu Nasional Teknologi Informasi dan Komunikasi untuk Indonesia, p. 1, 2008. [7] S. Darudiato, "Perancangan Data Warehouse Penjualan Untuk Mendukung Kebutuhan Informasi Eksekutif Cemerlang Skin Care," Seminar Nasional Informatika 2010, pp. E-353, 2010.

Generic visual data mining-based framework for revealing abnormal operation patterns in building energy systems

2021 ◽  
Vol 125 ◽  
pp. 103624
Author(s):  
Chaobo Zhang ◽  
Yang Zhao ◽  
Tingting Li ◽  
Xuejun Zhang ◽  
Meriem Adnouni
Keyword(s):  
Data Mining ◽  
Energy Systems ◽  
Building Energy ◽  
Visual Data ◽  
Visual Data Mining ◽  
Building Energy Systems ◽  
Abnormal Operation

Big Data Visualisation and Visual Analytics for Music Data Mining

2018 ◽  
Author(s):  
Katrina E. Barkwell ◽  
Alfredo Cuzzocrea ◽  
Carson K. Leung ◽  
Ashley A. Ocran ◽  
Jennifer M. Sanderson ◽  
...  
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Visual Analytics ◽  
Data Visualisation ◽  
Music Data Mining

Visual Data Mining

2018 ◽  
pp. 4491-4495
Author(s):  
Simeon J. Simoff
Keyword(s):  
Data Mining ◽  
Visual Data ◽  
Visual Data Mining
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