Demo Data from EDBT 2026 / reviewers in the wild / expert
Michael Glenister
dblp:228/5079
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 2018
—ORCID · unresolved
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 · 67% Computational science and engineering · 33% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Computational science and engineering › computational chemistry › molecular simulation › molecular dynamics
molecular dynamics analysis |
0.3 | 1 | 2018 | MODE-TASK: large-scale protein motion tools · Bioinform. 2018 |
Bioinformatics and computational biology › protein dynamics
protein dynamics analysis |
0.3 | 1 | 2018 | MODE-TASK: large-scale protein motion tools · Bioinform. 2018 |
Bioinformatics and computational biology › structural biology › protein structure and function
protein structure and dynamics |
0.3 | 1 | 2018 | MODE-TASK: large-scale protein motion tools · Bioinform. 2018 |
Methods — techniques the papers use, named apart from their topics
t-distributed stochastic neighbor embedding · 0.3principal component analysis · 0.3normal mode analysis · 0.3multidimensional scaling · 0.3anisotropic network model · 0.3
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2018 | MODE-TASK: large-scale protein motion toolsabstractSummary: MODE-TASK, a novel and versatile software suite, comprises Principal Component Analysis, Multidimensional Scaling, and t-Distributed Stochastic Neighbor Embedding techniques using Molecular Dynamics trajectories. MODE-TASK also includes a Normal Mode Analysis tool based on Anisotropic Network Model so as to provide a variety of ways to analyse and compare large-scale motions of protein complexes for which long MD simulations are prohibitive. Beside the command line function, a GUI has been developed as a PyMOL plugin. Availability and implementation: MODE-TASK is open source, and available for download from https://github.com/RUBi-ZA/MODE-TASK. It is implemented in Python and C++. It is compatible with Python 2.x and Python 3.x and can be installed by Conda. Supplementary information: Supplementary data are available at Bioinformatics online. Caroline J. Ross, Bilal Nizami, Michael Glenister, Olivier Sheik Amamuddy, Ali Rana Atilgan, Canan Atilgan, Özlem Tastan Bishop |
Bioinform. | 3 |