Evidence map›Paper›PMID 42141610›Full record

ArticlePhysical review. E2026

Fitness inference tested by in silico population genetics.

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

Abstract read
PubMed Publisher
In one paragraph

Article in Physical review. E, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Direct estimation of genotype fitness from time series.bioRxiv : the preprint server for biology · 2026
    Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

4 authors.

Hong-Li ZengNational Laboratory of Solid State Microstructures, Nanjing University of Posts and Telecommunications, School of Science, Nanjing University, Nanjing 210093, China and Key Laboratory of Radio and Micro-Nano Electronics of Jiangsu Province, Nanjing 210023, China.
Yu-Han HuangNational Laboratory of Solid State Microstructures, Nanjing University of Posts and Telecommunications, School of Science, Nanjing University, Nanjing 210093, China and Key Laboratory of Radio and Micro-Nano Electronics of Jiangsu Province, Nanjing 210023, China.
Erik AurellAlbaNova, Department of Computational Science and Technology, University Center, SE-106 91 Stockholm, Sweden.
John BartonUniversity of Pittsburgh, Department of Computational & Systems Biology, School of Medicine, Pittsburgh, Pennsylvania 15213, USA.

Funding

No grant is acknowledged in the PubMed record.

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 of whether 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

Computer SimulationGenetic FitnessGenetics, PopulationModels, GeneticAnimalsHumansMutationSelection, Genetic

Identifiers

PMID42141610

What OpenQuestion holds

Textmetadata
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.