A simulated annealing algorithm for maximum likelihood pedigree reconstruction

DOI10.1016/S0040-5809(02)00048-5zbMath1104.62115OpenAlexW2016936453WikidataQ42695762 ScholiaQ42695762MaRDI QIDQ849461

Anthony Almudevar

Publication date: 31 October 2006

Published in: Theoretical Population Biology (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/s0040-5809(02)00048-5

zbMATH Keywords

simulated annealingmaximum likelihoodpedigree reconstructionenumeration of feasible predigrees

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10) Problems related to evolution (92D15) Approximation methods and heuristics in mathematical programming (90C59)

Related Items (13)

Efficient maximum likelihood pedigree reconstruction ⋮ On the use of dense SNP marker data for the identification of distant relative pairs ⋮ Bayesian pedigree inference with small numbers of single nucleotide polymorphisms via a factor-graph representation ⋮ An information theoretic approach to pedigree reconstruction ⋮ A graphical approach to relatedness inference ⋮ Phenotype space and kinship assignment for the simpson index ⋮ Reconstruction of pedigrees in clonal plant populations ⋮ Improved maximum likelihood reconstruction of complex multi-generational pedigrees ⋮ Reconstruction of pedigrees in clonal plant populations ⋮ Capacitated clustering problem in computational biology: combinatorial and statistical approach for sibling reconstruction ⋮ On the choice of prior density for the Bayesian analysis of pedigree structure ⋮ On approximating four covering and packing problems ⋮ On Approximating an Implicit Cover Problem in Biology

Cites Work

• Optimization by Simulated Annealing
• The relationship between single parent and parent pair genetic likelihoods in genealogy reconstruction
• Partition-distance: A problem and class of perfect graphs arising in clustering
• A Bootstrap Assessment of Variability in Pedigree Reconstruction Based on Genetic Markers
• Gene Identities and Multiple Relationships
• Equation of State Calculations by Fast Computing Machines
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