Evidence map›Paper›PMID 39506375›Full record

ArticleSystematic biology2025

How to Validate a Bayesian Evolutionary Model.

Fábio K Mendes, Remco Bouckaert, Luiz M Carvalho, Alexei J Drummond

Abstract read
In one paragraph

Article in Systematic biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

14 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Fábio K MendesDepartment of Biological Sciences, Louisiana State University, Baton Rouge, LA 70803, USA.
Remco BouckaertSchool of Computer Science, The University of Auckland, Auckland 1010, New Zealand.
Luiz M CarvalhoEscola de Matemática Aplicada, Fundação Getulio Vargas, Rio de Janeiro, RJ 22250-900, Brazil.
Alexei J DrummondSchool of Biological Sciences, The University of Auckland, Auckland 1010, New Zealand.

Funding

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior-BrasilFundação Carlos Chagas Filho de Amparo à Pesquisa do Estado do Rio de JaneiroThe National Science Foundation DEB-2040347
6 · The paper itself

Abstract

Biology has become a highly mathematical discipline in which probabilistic models play a central role. As a result, research in the biological sciences is now dependent on computational tools capable of carrying out complex analyses. These tools must be validated before they can be used, but what is understood as validation varies widely among methodological contributions. This may be a consequence of the still embryonic stage of the literature on statistical software validation for computational biology. Our manuscript aims to advance this literature. Here, we describe, illustrate, and introduce new good practices for assessing the correctness of a model implementation with an emphasis on Bayesian methods. We also introduce a suite of functionalities for automating validation protocols. It is our hope that the guidelines presented here help sharpen the focus of discussions on (as well as elevate) expected standards of statistical software for biology.

Indexed as

Biological EvolutionClassificationComputational BiologyModels, BiologicalBayes TheoremSoftwareBayesian modelcoveragemodel validationProbabilistic model

Identifiers

PMID39506375
PMCPMC11809579

What OpenQuestion holds

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LicenceCC BY
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Registered trials

None linked

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.