Evidence map›Paper›PMID 37163049›Full record

ArticlemedRxiv : the preprint server for health sciences2023

Unsupervised representation learning improves genomic discovery and risk prediction for respiratory and circulatory functions and diseases.

Taedong Yun, Justin Cosentino, Babak Behsaz, Zachary R McCaw, Davin Hill, Robert Luben, Dongbing Lai, John Bates, Howard Yang, Tae-Hwi Schwantes-An and 7 more

Abstract readPreprint
PubMed Full text at PMC Publisher
In one paragraph

Article in medRxiv : the preprint server for health sciences, 2023. 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

17 authors.

Taedong YunGoogle Research, Cambridge, MA 02142, USA.
Justin CosentinoGoogle Research, Palo Alto, CA 94304, USA.
Babak BehsazGoogle Research, Cambridge, MA 02142, USA.
Zachary R McCawGoogle Research, Palo Alto, CA 94304, USA.
Davin HillDepartment of Electrical and Computer Engineering, Northeastern University, Boston, MA 94304, USA.
Robert LubenNIHR Biomedical Research Centre at Moorfields Eye Hospital & UCL Institute of Ophthalmology, London EC1V 9EL, UK.
Dongbing LaiDepartment of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN 46202, USA.
John BatesVerily Life Sciences, South San Francisco, CA 94080, USA.
Howard YangGoogle Research, Palo Alto, CA 94304, USA.
Tae-Hwi Schwantes-AnDepartment of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN 46202, USA.
Yuchen ZhouGoogle Research, Cambridge, MA 02142, USA.
Anthony P KhawajaNIHR Biomedical Research Centre at Moorfields Eye Hospital & UCL Institute of Ophthalmology, London EC1V 9EL, UK.
Andrew CarrollGoogle Research, Palo Alto, CA 94304, USA.
Brian D HobbsChanning Division of Network Medicine, Brigham and Women's Hospital, Boston, MA 02115, USA.
Michael H ChoChanning Division of Network Medicine, Brigham and Women's Hospital, Boston, MA 02115, USA.
Cory Y McLeanGoogle Research, Cambridge, MA 02142, USA.
Farhad HormozdiariGoogle Research, Cambridge, MA 02142, USA.

Funding

Genetic and Functional Dissection of a Cluster of COPD GWAS Signals on Chromosome 4qR01HL147148 · NHLBI · BRIGHAM AND WOMEN'S HOSPITAL · PI CHO, MICHAEL H., SILVERMAN, EDWIN K · 2019 to 2022
$3.5M
Clinical Implications, Genomics, and Transcriptions of Mucus Plugging in SmokersR01HL149861 · NHLBI · BRIGHAM AND WOMEN'S HOSPITAL · PI CHO, MICHAEL H., DIAZ, ALEJANDRO · 2020 to 2023
$3.2M
Uncovering the genetically-driven differential susceptibility to chronic obstructive pulmonary disease and pulmonary fibrosisR01HL162813 · NHLBI · BRIGHAM AND WOMEN'S HOSPITAL · PI MICHAEL H. CHO · 2023 to 2026
$3.1M
Integrative genomic, transcriptomic and proteomic studies of pulmonary function and COPDR01HL153248 · NHLBI · UNIVERSITY OF VIRGINIA · PI CHO, MICHAEL H., MANICHAIKUL, ANI WANG · 2021 to 2024
$2.8M
NHLBI NIH HHS R01 HL147148NHLBI NIH HHS R01 HL149861NHLBI NIH HHS R01 HL153248NHLBI NIH HHS R01 HL162813
6 · The paper itself

Abstract

High-dimensional clinical data are becoming more accessible in biobank-scale datasets. However, effectively utilizing high-dimensional clinical data for genetic discovery remains challenging. Here we introduce a general deep learning-based framework, REpresentation learning for Genetic discovery on Low-dimensional Embeddings (REGLE), for discovering associations between genetic variants and high-dimensional clinical data. REGLE uses convolutional variational autoencoders to compute a

Identifiers

PMID37163049
PMCPMC10168505

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.