Tracy A. Heath

Molecular Biology · 2002–2013

A researcher in the Science corpus: 3 papers; trusts 32 researchers and is trusted by 5 researchers here, as inferred from the citation graph.

Subfields

Whom Tracy A. Heath trusts overall

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

  1. Alexei J. Drummond
  2. Fredrik Ronquist
  3. Ziheng Yang
  4. Simon Y. W. Ho
  5. John P. Huelsenbeck
  6. Joseph Felsenstein
  7. Tanja Gernhard
  8. J. Bergsten
  9. Hirohisa Kishino
  10. Bruce Rannala

Who trusts Tracy A. Heath overall

  1. D. Wright
  2. N. Upham
  3. Michael J. Moore
  4. Alexandra Gavryushkina
  5. S. Magallón
  6. Roger B. J. Benson
  7. Joanna M. Wolfe
  8. John J. Wiens
  9. D. Cardoso
  10. Jacob A. Esselstyn

Papers from Tracy A. Heath's perspective

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

  1. MrBayes 3: Bayesian phylogenetic inference under mixed models (Bioinform.)
  2. MRBAYES: Bayesian inference of phylogenetic trees (Bioinform.)
  3. Evolutionary trees from DNA sequences: A maximum likelihood approach (Journal of Molecular Evolution)
  4. Relaxed Phylogenetics and Dating with Confidence (PLoS Biology)
  5. PAUP* Phylogenetic analysis using parsimony (*and other methods) Version 4
  6. CLUSTAL W: improving the sensitivity of progressive multiple sequence alignment through sequence weighting, position-specific gap penalties and weight matrix choice.
  7. Bayesian estimation of species divergence times under a molecular clock using multiple fossil calibrations with soft bounds. (Molecular biology and evolution)
  8. A simple, fast, and accurate algorithm to estimate large phylogenies by maximum likelihood. (Systematic Biology)
  9. A Bayesian mixture model for across-site heterogeneities in the amino-acid replacement process. (Molecular biology and evolution)
  10. A general empirical model of protein evolution derived from multiple protein families using a maximum-likelihood approach.

Their papers