Jean-François Dufayard

Molecular Biology · 2009–2011

A researcher in the Science corpus: 3 papers; trusts 13 researchers and is trusted by 12 researchers here, as inferred from the citation graph.

Subfields

Whom Jean-François Dufayard trusts overall

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

  1. Alexandros Stamatakis
  2. Maria Anisimova
  3. Stéphane Guindon
  4. Olivier Gascuel
  5. Joseph Felsenstein
  6. John P. Huelsenbeck
  7. Derrick J. Zwickl
  8. Nick Goldman
  9. Christian M. Zmasek
  10. Simon Whelan

Who trusts Jean-François Dufayard overall

  1. Bui Quang Minh
  2. Vincent Lefort
  3. Frédéric Lemoine
  4. A. von Haeseler
  5. Guangchuang Yu
  6. Akifumi S. Tanabe
  7. Barry G. Hall
  8. Morgan N. Price
  9. Lam-Tung Nguyen
  10. Jana Trifinopoulos

Papers from Jean-François Dufayard's perspective

Ordered by how strongly the researchers Jean-François Dufayard 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. Genetic algorithm approaches for the phylogenetic analysis of large biological sequence datasets under the maximum likelihood criterion
  3. MRBAYES: Bayesian inference of phylogenetic trees (Bioinform.)
  4. A new method for calculating evolutionary substitution rates (Journal of Molecular Evolution)
  5. A general empirical model of protein evolution derived from multiple protein families using a maximum-likelihood approach.
  6. Likelihood-based tests of topologies in phylogenetics. (Systematic Biology)
  7. Phylogenetic models of rate heterogeneity: a high performance computing perspective (Proceedings 20th IEEE International Parallel & Distributed Processing Symposium)
  8. MrBayes 3: Bayesian phylogenetic inference under mixed models (Bioinform.)
  9. Evolutionary trees from DNA sequences: A maximum likelihood approach (Journal of Molecular Evolution)
  10. New algorithms and methods to estimate maximum-likelihood phylogenies: assessing the performance of PhyML 3.0.

Their papers