Melissa J. Hubisz

Molecular Biology · 2005–2013

A researcher in the Science corpus: 8 papers; trusts 13 researchers and is trusted by 11 researchers here, as inferred from the citation graph.

Subfields

Whom Melissa J. Hubisz trusts overall

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

  1. Matthew Stephens
  2. Daniel Falush
  3. J. K. Pritchard
  4. John Hartigan
  5. Nick Patterson
  6. Gilles Guillot
  7. Lawrence R. Rabiner
  8. Jukka Corander
  9. John Wakeley
  10. Ziheng Yang

Who trusts Melissa J. Hubisz overall

  1. Jinliang Wang
  2. Braulio J. Soto-Cerda
  3. Martin Kircher
  4. Gregory M. Cooper
  5. Anil Raj
  6. Kriti Puniyani
  7. I. Baums
  8. C. Morrison
  9. Julian Catchen
  10. Jay J. Lunden

Papers from Melissa J. Hubisz's perspective

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

  1. Inference of population structure using multilocus genotype data: linked loci and correlated allele frequencies. (Genetics)
  2. A new statistical method for haplotype reconstruction from population data. (American Journal of Human Genetics)
  3. Accounting for decay of linkage disequilibrium in haplotype inference and missing-data imputation. (American Journal of Human Genetics)
  4. Generating samples under a Wright-Fisher neutral model of genetic variation (Bioinform.)
  5. A tutorial on hidden Markov models and selected applications in speech recognition
  6. Inference in molecular population genetics
  7. A fast and flexible statistical model for large-scale population genotype data: applications to inferring missing genotypes and haplotypic phase. (American Journal of Human Genetics)
  8. Bayesian identification of admixture events using multilocus molecular markers (Molecular Ecology)
  9. Modeling linkage disequilibrium and identifying recombination hotspots using single-nucleotide polymorphism data. (Genetics)
  10. Bayesian spatial modeling of genetic population structure (Computational statistics (Zeitschrift))

Their papers