Evidence map›Paper›PMID 39091765›Full record

ArticlebioRxiv : the preprint server for biology2024

AI-driven Discovery of Morphomolecular Signatures in Toxicology.

Guillaume Jaume, Thomas Peeters, Andrew H Song, Rowland Pettit, Drew F K Williamson, Lukas Oldenburg, Anurag Vaidya, Simone de Brot, Richard J Chen, Jean-Philippe Thiran and 3 more

Abstract readPreprint
PubMed Full text at PMC Publisher
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. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Review
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

13 authors.

Guillaume JaumeDepartment of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA.
Thomas PeetersDepartment of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA.
Andrew H SongDepartment of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA.
Rowland PettitDepartment of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA.
Drew F K WilliamsonDepartment of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA.
Lukas OldenburgDepartment of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA.
Anurag VaidyaDepartment of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA.
Simone de BrotInstitute of Animal Pathology, Vetsuisse, University of Bern, Switzerland.
Richard J ChenDepartment of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA.
Jean-Philippe ThiranSignal Processing Laboratory, EPFL, Lausanne, Switzerland.
Long Phi LeDepartment of Pathology, Massachusetts General Hospital, Harvard Medical School, Boston, MA.
Georg GerberDepartment of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA.
Faisal MahmoodDepartment of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA.

Funding

Multimodal and Generative AI for PathologyR35GM138216 · NIGMS · BROAD INSTITUTE, INC. · PI Faisal Mahmood · 2020 to 2026
$3.4M
Probabilistic deep learning models and integrated biological experiments for analyzing dynamic and heterogeneous microbiomesR35GM149270 · NIGMS · BRIGHAM AND WOMEN'S HOSPITAL · PI Georg Kurt Gerber · 2023 to 2026
$1.8M
NIGMS NIH HHS R35 GM138216NIGMS NIH HHS R35 GM149270
6 · The paper itself

Abstract

Early identification of drug toxicity is essential yet challenging in drug development. At the preclinical stage, toxicity is assessed with histopathological examination of tissue sections from animal models to detect morphological lesions. To complement this analysis, toxicogenomics is increasingly employed to understand the mechanism of action of the compound and ultimately identify lesion-specific safety biomarkers for which

Identifiers

PMID39091765
PMCPMC11291055

What Socratic 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 Socratic graph.