Evidence map›Paper›PMID 38076790›Full record

ArticlemedRxiv : the preprint server for health sciences2025

Proteome-wide model for human disease genetics.

Rose Orenbuch, Courtney A Shearer, Aaron W Kollasch, Hansen D Spinner, Thomas A Hopf, Lood van Niekerk, Dinko Franceschi, Mafalda Dias, Jonathan Frazer, Debora S Marks

Open access · greenAbstract readPreprint
In one paragraph

Article in medRxiv : the preprint server for health sciences, 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, 16 citations in OpenAlex.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

10 authors at 3 institutions in 2 countries.

Courtney A Shearer
Hansen D Spinner
Thomas A Hopf
Lood van Niekerk
Dinko Franceschi
Harvard University · USBroad Institute · USUniversitat Pompeu Fabra · ES

Funding

Making antibody generation rapid, scalable, and democratic through machine learning and continuous evolutionR01CA260415 · NCI · UNIVERSITY OF CALIFORNIA-IRVINE · PI KRUSE, ANDREW, LIU, CHANG C · 2020 to 2024
$8.4M
NCI NIH HHS R01 CA260415
6 · The paper itself

Abstract

Identifying variants driving disease accelerates both genetic diagnosis and therapeutic development, but missense variants still present a bottleneck as their effects are less straightforward than truncations or nonsense mutations. While computational prediction methods are sufficiently accurate to be of clinical value for variants in

Identifiers

PMID38076790
PMCPMC10705666
OpenAlexW4389081060

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.