Vincent Lefort

Molecular Biology · 2010–2019

A researcher in the Science corpus: 4 papers; trusts 16 researchers and is trusted by 17 researchers here, as inferred from the citation graph.

Subfields

Whom Vincent Lefort trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

  1. Stéphane Guindon
  2. Olivier Gascuel
  3. Morgan N. Price
  4. Richard Desper
  5. Alexandros Stamatakis
  6. Hirotogu Akaike
  7. Maria Anisimova
  8. Federico Abascal
  9. Jean-François Dufayard
  10. Koichiro Tamura

Who trusts Vincent Lefort overall

  1. Frédéric Lemoine
  2. Jan P. Meier-Kolthof
  3. Thu-Hien To
  4. Edison Ong
  5. Damien Correia
  6. Diego Darriba
  7. Olivier Gascuel
  8. Markus Göker
  9. Zhemin Zhou
  10. Bui Quang Minh

Papers from Vincent Lefort's perspective

Ordered by how strongly the researchers Vincent Lefort trusts endorse each paper. Same corpus, a different order for every researcher.

  1. A simple, fast, and accurate algorithm to estimate large phylogenies by maximum likelihood. (Systematic Biology)
  2. New algorithms and methods to estimate maximum-likelihood phylogenies: assessing the performance of PhyML 3.0.
  3. RAxML-VI-HPC: maximum likelihood-based phylogenetic analyses with thousands of taxa and mixed models
  4. A general empirical model of protein evolution derived from multiple protein families using a maximum-likelihood approach.
  5. Phylogenetic models of rate heterogeneity: a high performance computing perspective (Proceedings 20th IEEE International Parallel & Distributed Processing Symposium)
  6. Approximate likelihood-ratio test for branches: A fast, accurate, and powerful alternative. (Systematic Biology)
  7. A rapid bootstrap algorithm for the RAxML Web servers. (Systematic Biology)
  8. A new method for calculating evolutionary substitution rates (Journal of Molecular Evolution)
  9. MrBayes 3: Bayesian phylogenetic inference under mixed models (Bioinform.)
  10. Genetic algorithm approaches for the phylogenetic analysis of large biological sequence datasets under the maximum likelihood criterion

Their papers