Evidence map›Paper›PMID 25161243›Full record

ArticleBioinformatics (Oxford, England)2014

RidgeRace: ridge regression for continuous ancestral character estimation on phylogenetic trees.

Christina Kratsch, Alice C McHardy

Abstract read
In one paragraph

Article in Bioinformatics (Oxford, England), 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.

0numbers the graph read from it
0cells of the map it votes in
16citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

Who cites it

16 citing papers in PubMed.

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  10. Where Do We Stand in Regularization for Life Science Studies?Journal of computational biology : a journal of computational molecular cell biology · 2022
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  16. Coupling of diversification and pH adaptation during the evolution of terrestrial Thaumarchaeota.Proceedings of the National Academy of Sciences of the United States of America · 2015
    Article
4 · The record

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5 · Who and what money

Authors and funding

2 authors.

Christina KratschDepartment for Algorithmic Bioinformatics, Heinrich Heine University, Universitätsstr. 1, 40225 Düsseldorf, Germany.
Alice C McHardyDepartment for Algorithmic Bioinformatics, Heinrich Heine University, Universitätsstr. 1, 40225 Düsseldorf, Germany.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

motivationAncestral character state reconstruction describes a set of techniques for estimating phenotypic or genetic features of species or related individuals that are the predecessors of those present today. Such reconstructions can reach into the distant past and can provide insights into the history of a population or a set of species when fossil data are not available, or they can be used to test evolutionary hypotheses, e.g. on the co-evolution of traits. Typical methods for ancestral character state reconstruction of continuous characters consider the phylogeny of the underlying data and estimate the ancestral process along the branches of the tree. They usually assume a Brownian motion model of character evolution or extensions thereof, requiring specific assumptions on the rate of phenotypic evolution.

resultsWe suggest using ridge regression to infer rates for each branch of the tree and the ancestral values at each inner node. We performed extensive simulations to evaluate the performance of this method and have shown that the accuracy of its reconstructed ancestral values is competitive to reconstructions using other state-of-the-art software. Using a hierarchical clustering of gene mutation profiles from an ovarian cancer dataset, we demonstrate the use of the method as a feature selection tool. AVAILABILITY AND IMPLEMENTATION: The algorithm described here is implemented in C++ as a stand-alone program, and the source code is freely available at http://algbio.cs.uni-duesseldorf.de/software/RidgeRace.tar.gz. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Indexed as

PhylogenyAlgorithmsFemaleHumansLeast-Squares AnalysisMutationOvarian NeoplasmsPhenotypeSoftware

Identifiers

PMID25161243
PMCPMC4147928

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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.