Evidence map›Paper›PMID 41281199›Full record

ArticleArXiv2025

Fitness inference tested by in silico population genetics.

Hong-Li Zeng, Yu-Han Huang, John Barton, Erik Aurell

Abstract readPreprint
PubMed Full text at PMC
In one paragraph

Article in ArXiv, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

4 authors.

Hong-Li ZengSchool of Science, Nanjing University of Posts and Telecommunications, National Laboratory of Solid State Microstructures, Nanjing University, Nanjing 210093, CHINA; Key Laboratory of Radio and Micro-Nano Electronics of Jiangsu Province, Nanjing, 210023, CHINA.
Yu-Han HuangSchool of Science, Nanjing University of Posts and Telecommunications, National Laboratory of Solid State Microstructures, Nanjing University, Nanjing 210093, CHINA; Key Laboratory of Radio and Micro-Nano Electronics of Jiangsu Province, Nanjing, 210023, CHINA.
John BartonDepartment of Computational & Systems Biology, University of Pittsburgh School of Medicine, USA.
Erik AurellDepartment of Computational Science and Technology, AlbaNova University Center, SE-106 91 Stockholm, SWEDEN.

Funding

Methods for quantifying selection in evolving populationsR35GM138233 · NIGMS · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI BARTON, JOHN P · 2020 to 2024
$1.9M
NIGMS NIH HHS R35 GM138233
6 · The paper itself

Abstract

We consider populations evolving according to natural selection, mutation, and recombination, and assume that the genomes of all or a representative selection of individuals are known. We pose the problem if it is possible to infer fitness parameters and genotype fitness order from such data. We tested this hypothesis in simulated populations. We delineate parameter ranges where this is possible and other ranges where it is not. Our work provides a framework for determining when fitness inference is feasible from population-wide, whole-genome, time-stratified data and highlights settings where it is not. We give a brief survey of biological model organisms and human pathogens that fit into this framework.

Indexed as

EvolutionFitness inferenceMarginal path likelihood methodTransient quasi-linkage equilibrium method

Identifiers

PMID41281199
PMCPMC12633630

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.