Special Issue "Biological Data Visualization"

A special issue of Data (ISSN 2306-5729).

Deadline for manuscript submissions: closed (30 September 2018)

Special Issue Editors

Guest Editor
Professor Ben-Chang Shia

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

Biostatistics Center, Taipei Medical University, Taipei, Taiwan
Website | E-Mail
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 papers will be 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 quarterly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) is waived for well-prepared manuscripts submitted to this issue. 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 (1 paper)

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Research

Open AccessArticle CoeViz: A Web-Based Integrative Platform for Interactive Visualization of Large Similarity and Distance Matrices
Received: 2 December 2017 / Revised: 10 January 2018 / Accepted: 11 January 2018 / Published: 13 January 2018
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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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