Evidence map›Paper›PMID 39372505›Full record

ArticleBioinformatics and biology insights2024

Genetic Architectures of Medical Images Revealed by Registration of Multiple Modalities.

Sam Freesun Friedman, Gemma Elyse Moran, Marianne Rakic, Anthony Phillipakis

Abstract read
In one paragraph

Article in Bioinformatics and biology insights, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Article
  3. Consider this a WARNing.Patterns (New York, N.Y.) · 2024
    Article
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

4 authors.

Sam Freesun FriedmanData Sciences Platform, Broad Institute of MIT and Harvard, Cambridge, MA, USA.ORCID https://orcid.org/0000-0002-0688-2169
Gemma Elyse MoranStatistics Department, Rutgers University, New Brunswick, NJ, USA.
Marianne RakicComputer Science and Artificial Intelligence Laboratory, MIT, Cambridge, MA, USA.
Anthony PhillipakisData Sciences Platform, Broad Institute of MIT and Harvard, Cambridge, MA, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The advent of biobanks with vast quantities of medical imaging and paired genetic measurements creates huge opportunities for a new generation of genotype-phenotype association studies. However, disentangling biological signals from the many sources of bias and artifacts remains difficult. Using diverse medical images and time-series (ie, magnetic resonance imagings [MRIs], electrocardiograms [ECGs], and dual-energy X-ray absorptiometries [DXAs]), we show how registration, both spatial and temporal, guided by domain knowledge or learned

Indexed as

autoencodersGeneticsGWASmedical imagingmultimodalPheWASregistration

Identifiers

PMID39372505
PMCPMC11450573

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.