Evidence map›Paper›PMID 39149314›Full record

ArticlebioRxiv : the preprint server for biology2024

mosGraphGPT: a foundation model for multi-omic signaling graphs using generative AI.

Heming Zhang, Di Huang, Emily Chen, Dekang Cao, Tim Xu, Ben Dizdar, Guangfu Li, Yixin Chen, Philip Payne, Michael Province and 1 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. 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

11 authors.

Heming ZhangInstitute for Informatics, Data Science and Biostatistics (I2DB), Washington University School of Medicine.
Di HuangInstitute for Informatics, Data Science and Biostatistics (I2DB), Washington University School of Medicine.
Emily ChenInstitute for Informatics, Data Science and Biostatistics (I2DB), Washington University School of Medicine.
Dekang CaoInstitute for Informatics, Data Science and Biostatistics (I2DB), Washington University School of Medicine.
Tim XuInstitute for Informatics, Data Science and Biostatistics (I2DB), Washington University School of Medicine.
Ben DizdarInstitute for Informatics, Data Science and Biostatistics (I2DB), Washington University School of Medicine.
Guangfu LiDepartment of Surgery, School of Medicine, University of Connecticut, CT, 06032, USA.
Yixin ChenDepartment of Computer Science and Engineering.
Philip PayneInstitute for Informatics, Data Science and Biostatistics (I2DB), Washington University School of Medicine.
Michael ProvinceDivision of Statistical Genomics, Department of Genetics.
Fuhai LiInstitute for Informatics, Data Science and Biostatistics (I2DB), Washington University School of Medicine.

Funding

Systems-Level Approach to Neuronopathic Lysosomal Storage DisordersRM1NS132962 · NINDS · WASHINGTON UNIVERSITY · PI JONATHAN D COOPER, PATRICIA I DICKSON · 2023 to 2026
$6.3M
Modeling and targeting tumor-immune signaling interactions in tumor microenvironmentR01LM013902 · NLM · WASHINGTON UNIVERSITY · PI Fuhai Li · 2023 to 2026
$1.4M
Combine Genomics and Symptoms Data Driven Models to Discover Synergistic Combinatory Therapies for Alzheimer's DiseaseR56AG065352 · NIA · WASHINGTON UNIVERSITY · PI LI, FUHAI · 2020 to 2021
$986k
AI models of multi-omic data integration for ming longevity core signaling pathwaysR21AG078799 · NIA · WASHINGTON UNIVERSITY · PI LI, FUHAI, PROVINCE, MICHAEL A. · 2023 to 2023
$461k
NIA NIH HHS R21 AG078799NIA NIH HHS R56 AG065352NINDS NIH HHS RM1 NS132962NLM NIH HHS R01 LM013902
6 · The paper itself

Abstract

Generative pretrained models represent a significant advancement in natural language processing and computer vision, which can generate coherent and contextually relevant content based on the pre-training on large general datasets and fine-tune for specific tasks. Building foundation models using large scale omic data is promising to decode and understand the complex signaling language patterns within cells. Different from existing foundation models of omic data, we build a foundation model,

Identifiers

PMID39149314
PMCPMC11326168

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.