ArticleTheoretical population biology2012
Ascertainment correction for a population tree via a pruning algorithm for likelihood computation.
Article in Theoretical population biology, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
4 citing papers in PubMed.
- The Probability of Joint Monophyly of Samples of Gene Lineages for All Species in an Arbitrary Species Tree.Journal of computational biology : a journal of computational molecular cell biology · 2022Article
- Bayesian Divergence-Time Estimation with Genome-Wide Single-Nucleotide Polymorphism Data of Sea Catfishes (Ariidae) Supports Miocene Closure of the Panamanian Isthmus.Systematic biology · 2018Article
- The probability of monophyly of a sample of gene lineages on a species tree.Proceedings of the National Academy of Sciences of the United States of America · 2016Article
- Approximate Likelihood Estimation of Divergence Time Range Using a Coalescent-based Model.Evolutionary bioinformatics online · 2013Article
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
We present a method for correcting ascertainment-bias in a coalescent-based likelihood for population trees. Our method is computationally simple and fast. To correct for the bias we compute the probability of allele-counts conditioned on the locus being included. This conditional probability is simply the uncorrected likelihood divided by the inclusion probability. A modification of a pruning algorithm is introduced so that the inclusion probability can be computed with a single run of the algorithm. Our computation is exact and avoids Monte-Carlo based methods.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.