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Biological Data Visualization
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Biological Data Visualization

A special issue of Data (ISSN 2306-5729). This special issue belongs to the section "Computational Biology, Bioinformatics, and Biomedical Data Science".

Deadline for manuscript submissions: closed (30 September 2018) | Viewed by 9756

Special Issue Editors

Professor Ben-Chang Shia

E-Mail Website
Guest Editor
College of Management, Biological Technology EMBA, Taipei Medical University, Taipei, Taiwan
Interests: big data; AI; biostatistics; sampling survey; set up the way of predicting; data mining
Dr. Yen-Kuang Lin

E-Mail Website
Guest Editor
Biostatistics Center, Taipei Medical University, Taipei, Taiwan
Interests: multivariate statistical model; longitudinal data analysis; data visualization in clinical trial; statistical consultation

Special Issue Information

Dear Colleagues,

Data visualization aims to properly present data in a graphical format. Data management sets are the root of biological science, and corporate physicians’ decision-making process in practice. Data visualization enables practitioners to grasp complicated concepts retrieved from data management. The visual presentation of biological data has received increasing attention, from both academia and industry, since the development of supportive hardware and software. Integration of data management and data visualization can help decision makers to gain a significant advantage in core competencies. On the one hand, data analytics can transform mass data into summaries to solve practical needs. On the other hand, data visualization converts summaries into graphics, charts and animations to help decision makers to consume balk of complex summaries. As a result, today’s practitioners are to adopt a holistic approach to optimize ways of presenting informative data across functional units.

This Special Issue on “Biological Data Visualization” is intended to present recent advances in data visualization in biological fields. Authors are encouraged to submit applied articles addressing this theme in this Special Issue. Analytical models, and case studies are all welcomed. Topics include, but are not limited to, the following research topics:

  • Data-driven visualization methodologies for biology
  • Development of data visualization techniques and interface
  • Impact of data visualization on decision-making process regarding biology
  • Optimization of complex data presentation and core information conveyed
  • Visual presentation of data mining results in biology
  • Visual presentation for feature extraction and selection
  • Data visualization for data management and data quality
  • Data visualization as a means for biology education

Prof. Dr. Ben-Chang Shia
Dr. Yen-Kuang Lin
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Data is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (2 papers)

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Research

7 pages, 1360 KiB  
Article
Visualization of Myocardial Strain Pattern Uniqueness with Respect to Activation Time and Contractility: A Computational Study
by Borut Kirn
Data 2019, 4(2), 79; https://doi.org/10.3390/data4020079 - 24 May 2019
Viewed by 2622
Abstract
Speckle tracking echography is used to measure myocardial strain patterns in order to assess the state of myocardial tissue. Because electro-mechanical coupling in myocardial tissue is complex and nonlinear, and because of the measurement errors the uniqueness of strain patterns is questionable. In [...] Read more.
Speckle tracking echography is used to measure myocardial strain patterns in order to assess the state of myocardial tissue. Because electro-mechanical coupling in myocardial tissue is complex and nonlinear, and because of the measurement errors the uniqueness of strain patterns is questionable. In this study, the uniqueness of strain patterns was visualized in order to revel characteristics that may improve their interpretation. A computational model of sarcomere mechanics was used to generate a database of 1681 strain patterns, each simulated with a different set of sarcomere parameters: time of activation (TA) and contractility (Con). TA and Con ranged from −100 ms to 100 ms and 2% to 202% in 41 steps respectively, thus forming a two-dimensional 41 × 41 parameter space. Uniqueness of the strain pattern was assessed by using a cohort of similar strain patterns defined by a measurement error. The cohort members were then visualized in the parameter space. Each cohort formed one connected component (or blob) in the parameter space; however, large differences in the shape, size, and eccentricity of the blobs were found for different regions in the parameter space. The blobs were elongated along the TA direction (±50 ms) when contractility was low, and along the Con direction (±50%) when contractility was high. The uniqueness of the strain patterns can be assessed and visualized in the parameter space. The strain patterns in the studied database are not degenerated because a cohort of similar strain patterns forms only one connected blob in the parameter space. However, the elongation of the blobs means that estimations of TA when contractility is low and of Con when contractility is high have high uncertainty. Full article
(This article belongs to the Special Issue Biological Data Visualization)
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10 pages, 8730 KiB  
Article
CoeViz: A Web-Based Integrative Platform for Interactive Visualization of Large Similarity and Distance Matrices
by Frazier N. Baker and Aleksey Porollo
Data 2018, 3(1), 4; https://doi.org/10.3390/data3010004 - 13 Jan 2018
Cited by 7 | Viewed by 6434
Abstract
Similarity and distance matrices are general data structures that describe reciprocal relationships between the objects within a given dataset. Commonly used methods for representation of these matrices include heatmaps, hierarchical trees, dimensionality reduction, and various types of networks. However, despite a well-developed foundation [...] Read more.
Similarity and distance matrices are general data structures that describe reciprocal relationships between the objects within a given dataset. Commonly used methods for representation of these matrices include heatmaps, hierarchical trees, dimensionality reduction, and various types of networks. However, despite a well-developed foundation for the visualization of such representations, the challenge of creating an interactive view that would allow for quick data navigation and interpretation remains largely unaddressed. This problem becomes especially evident for large matrices with hundreds or thousands objects. In this work, we present a web-based platform for the interactive analysis of large (dis-)similarity matrices. It consists of four major interconnected and synchronized components: a zoomable heatmap, interactive hierarchical tree, scalable circular relationship diagram, and 3D multi-dimensional scaling (MDS) scatterplot. We demonstrate the use of the platform for the analysis of amino acid covariance data in proteins as part of our previously developed CoeViz tool. The web-platform enables quick and focused analysis of protein features, such as structural domains and functional sites. Full article
(This article belongs to the Special Issue Biological Data Visualization)
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