Kai F. Müller

Molecular Biology · 2003–2011

A researcher in the Science corpus: 8 papers; trusts 15 researchers and is trusted by 46 researchers here, as inferred from the citation graph.

Subfields

Whom Kai F. Müller trusts overall

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

  1. Hirotugu Akaike
  2. Christian M. Zmasek
  3. Wayne P. Maddison
  4. Mark T. Holder
  5. John P. Huelsenbeck
  6. Sudhir Kumar
  7. S. Kelchner
  8. François Chevenet
  9. Michael P. Cummings
  10. Paul O. Lewis

Who trusts Kai F. Müller overall

  1. Thomas Borsch
  2. Cornelia Löhne
  3. Susy Fuentes-Bazan
  4. J. Wiersema
  5. J. Zahniser
  6. Stefan Wanke
  7. J. Zimmermann
  8. G. Kadereit
  9. K. Hilu
  10. Andreas Worberg

Papers from Kai F. Müller's perspective

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

  1. Overcredibility of molecular phylogenies obtained by Bayesian phylogenetics (Proceedings of the National Academy of Sciences of the United States of America)
  2. Bayesian Inference of Phylogeny and Its Impact on Evolutionary Biology (Science)
  3. Analysis of 81 genes from 64 plastid genomes resolves relationships in angiosperms and identifies genome-scale evolutionary patterns (Proceedings of the National Academy of Sciences of the United States of America)
  4. Morphological Phylogenetic Analysis of Basal Angiosperms: Comparison and Combination with Molecular Data (INTERNATIONAL JOURNAL PLANT SCIENCES)
  5. Plastid genome sequencing, comparative genomics, and phylogenomics: Current status and prospects
  6. ATV: display and manipulation of annotated phylogenetic (Bioinform.)
  7. MEGA3: Integrated software for Molecular Evolutionary Genetics Analysis and sequence alignment (Briefings Bioinform.)
  8. An approximately unbiased test of phylogenetic tree selection. (Systematic Biology)
  9. Potential applications and pitfalls of Bayesian inference of phylogeny. (Systematic Biology)
  10. Phylogeny estimation: traditional and Bayesian approaches (Nature reviews genetics)

Their papers