EDBT 2026 Demo / reviewers in the wild / expert
Duong Vu
dblp:157/4827
· DBLP profile ↗
1ranked-venue papers
1as first author
0since 2021 · last 2018
0000-0001-7960-2765ORCID · reported
Domains — the database's venue-derived domains; a paper can count in several
Applied, interdisciplinary, general and emerging computing · 1 · 1 first-author
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 2 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Bioinformatics and computational biology › sequence analysis › sequence clustering
DNA sequence clustering |
0.3 | 1 | 2018 | fMLC: fast multi-level clustering and visualization of large molecular datasets · Bioinform. 2018 |
Bioinformatics and computational biology
sequence analysis |
0.3 | 1 | 2018 | fMLC: fast multi-level clustering and visualization of large molecular datasets · Bioinform. 2018 |
Methods — techniques the papers use, named apart from their topics
multi-threaded clustering · 0.3interactive web-based visualization · 0.3
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2018 | fMLC: fast multi-level clustering and visualization of large molecular datasetsabstractMotivation: Despite successful applications of data clustering and visualization techniques in molecular sequence identification, current technologies still do not scale to large biological datasets. Results: We address this problem by a new multi-threaded tool, fMLC, primarily developed to cluster DNA sequences, that is supplemented with an interactive web-based visualization component, DiVE. fMLC enabled to compare, cluster and visualize 350K ITS fungal sequences at the species level. It took less than two hours to compare and cluster the dataset, which is twelve times faster than the time reported previously. Availability and implementation: https://github.com/FastMLC/fMLC (doi: 10.5281/zenodo.926820). Contact: [email protected] or [email protected]. Duong Vu, Sonja Georgievska, Szaniszlo Szoke, Arnold Kuzniar, Vincent Robert |
Bioinform. | 1 |