Jacques Rougemont

Molecular Biology · 2004–2015

A researcher in the Science corpus: 4 papers; trusts 12 researchers and is trusted by 49 researchers here, as inferred from the citation graph.

Subfields

Whom Jacques Rougemont trusts overall

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

  1. Alexandros Stamatakis
  2. Peter E. Kloeden
  3. Desmond John Higham
  4. Derrick J. Zwickl
  5. Stéphane Guindon
  6. Fredrik Ronquist
  7. Eckhard Platen

Who trusts Jacques Rougemont overall

  1. Asha J. Dissanayake
  2. R. Zahiri
  3. Arnulf Jentzen
  4. L. Beenken
  5. Prashant P. Sharma
  6. Gonzalo Giribet
  7. Bui Quang Minh
  8. Robin Kundrata
  9. Romina Gazis
  10. K. D. Hyde

Papers from Jacques Rougemont's perspective

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

  1. Genetic algorithm approaches for the phylogenetic analysis of large biological sequence datasets under the maximum likelihood criterion
  2. A simple, fast, and accurate algorithm to estimate large phylogenies by maximum likelihood. (Systematic Biology)
  3. Likelihood-based tests of topologies in phylogenetics. (Systematic Biology)
  4. RAxML-III: a fast program for maximum likelihood-based inference of large phylogenetic trees (Bioinform.)
  5. MrBayes 3: Bayesian phylogenetic inference under mixed models (Bioinform.)
  6. Evolutionary trees from DNA sequences: A maximum likelihood approach (Journal of Molecular Evolution)
  7. Phylogenetic models of rate heterogeneity: a high performance computing perspective (Proceedings 20th IEEE International Parallel & Distributed Processing Symposium)
  8. LINEAR-IMPLICIT STRONG SCHEMES FOR ITO-GALKERIN APPROXIMATIONS OF STOCHASTIC PDES
  9. Greengenes, a Chimera-Checked 16S rRNA Gene Database and Workbench Compatible with ARB (Applied and Environmental Microbiology)
  10. RAxML-VI-HPC: maximum likelihood-based phylogenetic analyses with thousands of taxa and mixed models

Their papers