Evidence map›Paper›PMID 38586047›Full record

ArticlebioRxiv : the preprint server for biology2024

Faster inference of complex demographic models from large allele frequency spectra.

Enes Dilber, Jonathan Terhorst

Abstract readPreprint
In one paragraph

Article in bioRxiv : the preprint server for biology, 2024. 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

2 authors.

Enes DilberDepartment of Statistics, University of Michigan.ORCID 0000-0002-6292-9267
Jonathan TerhorstDepartment of Statistics, University of Michigan.ORCID 0000-0001-7765-2101

Funding

Large-scale phylodynamics under non-neutral and non-treelike models of evolutionR35GM151145 · NIGMS · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI Jonathan G Terhorst · 2023 to 2026
$1.5M
NIGMS NIH HHS R35 GM151145
6 · The paper itself

Abstract

We present momi3, a new method for inferring complex demographic models using genetic variation data sampled from many populations. momi3 features many improvements over its predecessor momi2 (Kamm, Terhorst, Durbin, et al., 2020), including support for continuous migration, just-in-time compilation, and execution on GPUs; a standardized interface for specifying demographic models; and a novel importance sampling strategy that enables it to efficiently analyze data from a large number of samples. Together, these improvements lead to speedups of as much as 1000× over existing state-of-the-art methods such as

Identifiers

PMID38586047
PMCPMC10996679

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.