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Lounès Chikhi

dblp:73/7066 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2009
0000-0002-1140-0718ORCID · reported

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 2 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Bioinformatics and computational biology › population genetics
admixture analysis
0.112009
A novel parallel approach to the likelihood-based estimation of admixture in population genetics · Bioinform. 2009
Bioinformatics and computational biology
population genetics
0.112009
A novel parallel approach to the likelihood-based estimation of admixture in population genetics · Bioinform. 2009

Methods — techniques the papers use, named apart from their topics

monte carlo simulation · 0.1message passing interface · 0.1
YearPublicationVenuePosition
2009 A novel parallel approach to the likelihood-based estimation of admixture in population genetics
abstract
Abstract Summary: Inferring population admixture from genetic data and quantifying it is a difficult but crucial task in evolutionary and conservation biology. Unfortunately state-of-the-art probabilistic approaches are computationally demanding. Effectively exploiting the computational power of modern multiprocessor systems can thus have a positive impact to Monte Carlo-based simulation of admixture modeling. A novel parallel approach is briefly described and promising results on its message passing interface (MPI)-based C++ implementation are reported. Availability: The software package parLEA is freely available at http://dm.unife.it/parlea. Contact: [email protected] Supplementary information: Additional information, including instructions for installation/use the original sequential LEA code and the data used in this paper, are also available in the web site.
Ambra Giovannini, Gaetano Zanghirati, Mark A. Beaumont, Lounès Chikhi, Guido Barbujani
Bioinform.4