Demo Data from EDBT 2026 / reviewers in the wild / expert
Himanshu Jotwani
dblp:277/0135
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 2020
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Applied, interdisciplinary, general and emerging computing · 1
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Interdisciplinary, comprehensive, and emerging computing
1 paper |
Bioinformatics and computational biology · 100% |
Topics — the 3 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Bioinformatics and computational biology › network bioinformatics › biological network analysis
network visualization |
0.4 | 1 | 2020 | ccNetViz: a WebGL-based JavaScript library for visualization of large networks · Bioinform. 2020 |
Bioinformatics and computational biology › network bioinformatics › biological network analysis › network visualization
biological network visualization |
0.1 | 1 | 2020 | ccNetViz: a WebGL-based JavaScript library for visualization of large networks · Bioinform. 2020 |
Bioinformatics and computational biology
systems biology |
0.1 | 1 | 2020 | ccNetViz: a WebGL-based JavaScript library for visualization of large networks · Bioinform. 2020 |
Methods — techniques the papers use, named apart from their topics
javascript · 0.4WebGL · 0.4
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2020 | ccNetViz: a WebGL-based JavaScript library for visualization of large networksabstractSUMMARY: Visualizing a network provides a concise and practical understanding of the information it represents. Open-source web-based libraries help accelerate the creation of biologically based networks and their use. ccNetViz is an open-source, high speed and lightweight JavaScript library for visualization of large and complex networks. It implements customization and analytical features for easy network interpretation. These features include edge and node animations, which illustrate the flow of information through a network as well as node statistics. Properties can be defined a priori or dynamically imported from models and simulations. ccNetViz is thus a network visualization library particularly suited for systems biology. AVAILABILITY AND IMPLEMENTATION: The ccNetViz library, demos and documentation are freely available at http://helikarlab.github.io/ccNetViz/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. Ales Saska, David Tichy, Achilles Rasquinha, Caner Akdas, Renato Fabbri, Ana Jelicic, Gaurav Grover, Himanshu Jotwani, Mohamed Shadab, Resa M. K. Helikar, Tomás Helikar, Jonathan D. Wren |
Bioinform. | 10 |