Evidence map›Paper›PMID 41415368›Full record

ArticlebioRxiv : the preprint server for biology2025

INTERPRETING CONVOLUTIONAL NEURAL NETWORKS IN POPULATION GENETICS.

Huiting Xu, Leon Zong, Dylan D Ray, Lei Lei, Daniel R Schrider, Franz Baumdicker, Sara Mathieson

Abstract readPreprint
PubMed Full text at PMC Publisher
In one paragraph

Article in bioRxiv : the preprint server for biology, 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

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

7 authors.

Huiting XuInstitute for Bioinformatics and Medical Informatics (IBMI), University of Tübingen, 72076 Tübingen, Germany.
Leon ZongDepartment of Biology, University of Pennsylvania, Philadelphia, PA.ORCID 0009-0000-3334-2742
Dylan D RayDepartment of Genetics, University of North Carolina at Chapel Hill.
Lei LeiDepartment of Computer Science, Haverford College, Haverford, PA.
Daniel R SchriderDepartment of Genetics, University of North Carolina at Chapel Hill.ORCID 0000-0001-5249-4151
Franz BaumdickerInstitute for Bioinformatics and Medical Informatics (IBMI), University of Tübingen, 72076 Tübingen, Germany.
Sara MathiesonDepartment of Biology, University of Pennsylvania, Philadelphia, PA.ORCID 0000-0002-0484-0838

Funding

Advancing evolutionary genetics through deep learningR35GM138286 · NIGMS · UNIV OF NORTH CAROLINA CHAPEL HILL · PI DANIEL R SCHRIDER · 2020 to 2026
$2.8M
Adaptive evolutionary inference frameworks for understudied populations using generative neural networksR15HG011528 · NHGRI · HAVERFORD COLLEGE · PI MATHIESON, SARA · 2021 to 2024
$783k
NHGRI NIH HHS R15 HG011528NIGMS NIH HHS R35 GM138286
6 · The paper itself

Abstract

Machine learning approaches have become a powerful alternative to traditional methods in population genetics. Convolutional neural networks (CNNs) in particular have been successful in inferring natural selection, recombination rate estimation, introgression, dispersal distances, and effective population size changes. One limitation of CNNs and other deep learning methods is that they can be difficult to interpret. When they have been shown to be as or more successful than summary-statistic-based methods, what are they learning? Here we investigate CNNs from two different methods: the

Indexed as

Convolutional neural networksMachine Learning interpretabilityPopulation genetics

Identifiers

PMID41415368
PMCPMC12710823

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.