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Francis Rodier

dblp:96/2837 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2008
—ORCID · none

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

TopicWeightPapersLastEvidence papers
Bioinformatics and computational biology
machine learning classification
0.112008
DiMoVo: a Voronoi tessellation-based method for discriminating crystallographic and biological protein-protein interactions · Bioinform. 2008
Bioinformatics and computational biology › protein analysis
protein-protein interaction
0.112008
DiMoVo: a Voronoi tessellation-based method for discriminating crystallographic and biological protein-protein interactions · Bioinform. 2008
Computational science and engineering › computational geometry
voronoi tessellation
0.112008
DiMoVo: a Voronoi tessellation-based method for discriminating crystallographic and biological protein-protein interactions · Bioinform. 2008

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

voronoi tessellation · 0.1machine learning · 0.1
YearPublicationVenuePosition
2008 DiMoVo: a Voronoi tessellation-based method for discriminating crystallographic and biological protein-protein interactions
abstract
MOTIVATION: Knowledge of the oligomeric state of a protein is often essential for understanding its function and mechanism. Within a protein crystal, each protein monomer is in contact with many others, forming many small interfaces and a few larger ones that are biologically significant if the protein is a homodimer in solution, but not if the protein is monomeric. Telling such 'crystal dimers' from real ones remains a difficult task. RESULTS: It has already been demonstrated that the interfaces of native and non-native protein-protein complexes can be distinguished using a combination of parameters computed with a method on the Voronoi tessellation. We show in this article that the same parameters highlight significant differences between the interfaces of biological and crystal dimers. Using these parameters as descriptors in machine learning methods leads to accurate classification of specific and non-specific protein-protein interfaces. AVAILABILITY: Software is available at http://fifi.ibbmc.u-psud.fr/DiMoVo.
Julie Bernauer, Ranjit Prasad Bahadur, Francis Rodier, Joël Janin, Anne Poupon
Bioinform.3