6 Papers
27 Citations
Simon Marillet is an academic researcher from French Institute for Research in Computer Science and Automation. The author has contributed to research in topics: Statistical classification & Cross-sectional study. The author has an hindex of 5, co-authored 5 publications. Previous affiliations of Simon Marillet include Université Paris-Saclay & Institut national de la recherche agronomique.
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Papers
Ciruvis: a web-based tool for rule networks and interaction detection using rule-based classifiers
TL;DR: Rule networks enable a fast method for model visualization and provide an exploratory heuristic to interaction detection and may be used to aid and improve rule-based classification.
Origin of Public Memory B Cell Clones in Fish After Antiviral Vaccination.
Susana Magadán,Luc Jouneau,Maximilian Puelma Touzel,Simon Marillet,Simon Marillet,Wahiba Chara,Adrien Six,Edwige Quillet,Thierry Mora,Aleksandra M. Walczak,Frédéric Cazals,Oriol Sunyer,Simon Fillatreau,Simon Fillatreau,Pierre Boudinot +14 more
TL;DR: The comparative approach identifies three conserved features of the antibody repertoire associated with public memory responses that were already present in the last common ancestors of fish and mammals, while other characteristics may represent species-specific solutions.
Self-reported dual sensory impairment and related factors: a European population-based cross-sectional survey
Nicolas Leveziel,Simon Marillet,Tasanee Braithwaite,Tunde Peto,Pierre Ingrand,Shahina Pardhan,Alain M. Bron,Jost B. Jonas,Serge Resnikoff,Little Julie Anne,Adrian Davis,Catherine McMahon,Rupert R A Bourne +12 more
TL;DR: In this article , a standardised questionnaire was used to collect medical and socio-economic data among individuals aged 15 years or more in 29 European countries, and the survey included 153,866 respondents aged 50 years old or more.
High‐resolution crystal structures leverage protein binding affinity predictions
TL;DR: This work suggests that the affinity prediction problem could be partly solved using databases of high resolution complexes whose affinity is known, and argues that interface flexibility and prediction hardness do not correlate, and that for flexible cases, a performance matching that of the whole SAB can be achieved.
Predicting binding poses and affinities for protein-ligand complexes in the 2015 D3R Grand Challenge using a physical model with a statistical parameter estimation
Sergei Grudinin,Maria Kadukova,Maria Kadukova,Maria Kadukova,Andreas Eisenbarth,Andreas Eisenbarth,Andreas Eisenbarth,Simon Marillet,Frédéric Cazals +8 more
TL;DR: The 2015 D3R Grand Challenge provided an opportunity to test the new model for the binding free energy of small molecules, as well as to assess the protocol to predict binding poses for protein-ligand complexes, which performed very well on the training set, however, failed on the two test sets.