Duong Vu

dblp:157/4827 · DBLP profile ↗
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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

TopicWeightPapersLastEvidence papers
Bioinformatics and computational biology › sequence analysis › sequence clustering
DNA sequence clustering
0.312018
fMLC: fast multi-level clustering and visualization of large molecular datasets · Bioinform. 2018
Bioinformatics and computational biology
sequence analysis
0.312018
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
YearPublicationVenuePosition
2018 fMLC: fast multi-level clustering and visualization of large molecular datasets
abstract
Motivation: 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