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Mohammed Sedki

dblp:124/6281 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2019
—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
Computational science and engineering · 56% Bioinformatics and computational biology · 44%

Topics — the 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Bioinformatics and computational biology › gene expression analysis
model-based clustering		 0.412019
VarSelLCM: an R/C++ package for variable selection in model-based clustering of mixed-data with missing values · Bioinform. 2019
Computational science and engineering › model selection
variable selection		 0.412019
VarSelLCM: an R/C++ package for variable selection in model-based clustering of mixed-data with missing values · Bioinform. 2019
Computational science and engineering › latent variable model
mixture model		 0.112019
VarSelLCM: an R/C++ package for variable selection in model-based clustering of mixed-data with missing values · Bioinform. 2019

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

mixture model · 0.4information criteria · 0.4
YearPublicationVenuePosition
2019 VarSelLCM: an R/C++ package for variable selection in model-based clustering of mixed-data with missing values
abstract
SUMMARY: VarSelLCM allows a full model selection (detection of the relevant features for clustering and selection of the number of clusters) in model-based clustering, according to classical information criteria. Data to be analyzed can be composed of continuous, integer and/or categorical features. Moreover, missing values are managed, without any pre-processing, by the model used to cluster with the assumption that values are missing completely at random. Thus, VarSelLCM also allows data imputation by using mixture models. A Shiny application is implemented to easily interpret the clustering results. AVAILABILITY AND IMPLEMENTATION: VarSelLCM is available to download at https://CRAN.R-project.org/package=VarSelLCM/. TUTORIAL: vignette is available online at http://varsellcm.r-forge.r-project.org/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Matthieu Marbac, Mohammed Sedki
 Bioinform.2