Evidence map›Paper›PMID 38979338›Full record

ArticlebioRxiv : the preprint server for biology2024

ClOneHORT: Approaches for Improved Fidelity in Generative Models of Synthetic Genomes.

Roland Laboulaye, Victor Borda, Shuo Chen, Kari E North, Robert Kaplan, Timothy D O'Connor

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

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

6 authors.

Roland LaboulayeInstitute for Genome Sciences, University of Maryland School of Medicine, Baltimore, 21201, USA.ORCID 0000-0002-4740-6548
Victor BordaInstitute for Genome Sciences, University of Maryland School of Medicine, Baltimore, 21201, USA.ORCID 0000-0003-3209-3961
Shuo ChenInstitute for Health Computing, University of Maryland School of Medicine, Baltimore, 21201, USA.
Kari E NorthDepartment of Epidemiology, University of North Carolina, Chapel Hill, NC, 27516, USA.ORCID 0000-0002-8903-0366
Robert KaplanAlbert Einstein College of Medicine, New York, NY, 10461, USA.
Timothy D O'ConnorInstitute for Genome Sciences, University of Maryland School of Medicine, Baltimore, 21201, USA.ORCID 0000-0002-0276-1896

Funding

Modeling the impact of Women's Specific Health Factors in PD outcomes in LatinasR01NS112499 · NINDS · CLEVELAND CLINIC LERNER COM-CWRU · PI FERNANDEZ MATA, IGNACIO · 2020 to 2024
$3.3M
Rapid Evolution of Genomic Architecture and Multi-omics TraitsR35HG010692 · NHGRI · UNIVERSITY OF MARYLAND BALTIMORE · PI O'CONNOR, TIMOTHY DAVID · 2019 to 2023
$2.3M
NHGRI NIH HHS R35 HG010692NINDS NIH HHS R01 NS112499
6 · The paper itself

Abstract

Motivation: Deep generative models have the potential to overcome difficulties in sharing individual-level genomic data by producing synthetic genomes that preserve the genomic associations specific to a cohort while not violating the privacy of any individual cohort member. However, there is significant room for improvement in the fidelity and usability of existing synthetic genome approaches. Results: We demonstrate that when combined with plentiful data and with population-specific selection criteria, deep generative models can produce synthetic genomes and cohorts that closely model the original populations. Our methods improve fidelity in the site-frequency spectra and linkage disequilibrium decay and yield synthetic genomes that can be substituted in downstream local ancestry inference analysis, recreating results with .91 to .94 accuracy.

Indexed as

deep generative modelsgenomicsprivacyvariational autoencoder

Identifiers

PMID38979338
PMCPMC11230377

What OpenQuestion holds

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LicenceCC BY-NC-SA
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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.