Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Genomics

Genomics

Genomics is the science of genomes: it is the study of all the genetic material of an organism. In humans, the genome consists of information carried in 23 pairs of chromosomes in the nucleus, as well as mitochondrial DNA. In genomics, both coding and non-coding DNA is sequenced and analyzed. Genomics allows a better understanding of all living things, their evolution, and their diversity. It has a myriad of uses: for example, to build phylogenetic trees, to improve productivity and...

Signal Flow Graphs

Signal Flow Graphs

Signal-flow graphs offer a streamlined and intuitive approach to representing control systems, providing an alternative to traditional block diagrams. These graphs use branches to symbolize systems and nodes to represent signals, effectively illustrating the relationships and interactions within the system.
In a signal-flow graph, branches denote the system's transfer functions, while nodes represent the signals. The direction of signal flow is indicated by arrows, with the corresponding...

Sequence Networks of Rotating Machines

Sequence Networks of Rotating Machines

A Y-connected synchronous generator, grounded through a neutral impedance, is designed to produce balanced internal phase voltages with only positive-sequence components. The generator's sequence networks include a source voltage that is exclusively in the positive-sequence network. The sequence components of line-to-ground voltages at the generator terminals illustrate this configuration.
Zero-sequence current induces a voltage drop across the generator's neutral impedance and other...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

OmniCellTOSG: The First Cell Text-Omic Signaling Graphs Dataset for Graph Language Foundation Modeling.

Research square·2026

Same author

MosGraphFlow: a novel integrative graph AI model mining signaling targets from multi-omic data.

BMC methods·2025

Same author

mosGraphGen: a novel tool to generate multi-omics signaling graphs to facilitate integrative and interpretable graph AI model development.

Bioinformatics advances·2024

Same author

mosGraphFlow: a novel integrative graph AI model mining disease targets from multi-omic data.

bioRxiv : the preprint server for biology·2024

Same author

mosGraphGen: a novel tool to generate multi-omics signaling graphs to facilitate integrative and interpretable graph AI model development.

bioRxiv : the preprint server for biology·2024

Same author

Mitochondrial Dysfunction and Impaired Antioxidant Responses in Retinal Pigment Epithelial Cells Derived from a Patient with <i>RCBTB1</i>-Associated Retinopathy.

Cells·2023

Search research articles

Related Experiment Video

Updated: May 10, 2026

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Published on: December 7, 2021

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

Heming Zhang¹, Di Huang¹, Emily Chen^1,2,3

¹Institute for Informatics, Data Science and Biostatistics (I2DB), Washington University School of Medicine.

Biorxiv : the Preprint Server for Biology

|August 16, 2024

Summary

This summary is machine-generated.

This study introduces mosGraphGPT, a novel foundation model for multi-omic signaling graphs. It improves disease classification accuracy and interpretability by analyzing complex cellular signaling patterns.

More Related Videos

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports

Published on: October 13, 2023

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Published on: March 1, 2024

Related Experiment Videos

Last Updated: May 10, 2026

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Published on: December 7, 2021

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports

Published on: October 13, 2023

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Published on: March 1, 2024

Area of Science:

Computational Biology
Bioinformatics
Genomics

Background:

Generative pretrained models excel in NLP and computer vision.
Foundation models for omics data can decode cellular signaling patterns.
Existing models lack comprehensive integration of multi-omic data.

Purpose of the Study:

To develop mosGraphGPT, a foundation model for multi-omic signaling (mos) graphs.
To integrate and interpret multi-omic data using a multi-level signaling graph.
To apply the model to cancer and Alzheimer's Disease data.

Main Methods:

Pre-training mosGraphGPT on The Cancer Genome Atlas (TCGA) multi-omic cancer data.
Fine-tuning the model on multi-omic data from Alzheimer's Disease (AD) studies.
Utilizing a multi-level signaling graph for data integration and interpretation.

Main Results:

The model significantly improved disease classification accuracy.
mosGraphGPT demonstrated interpretability by identifying disease targets and signaling interactions.
The developed model code is publicly available on GitHub.

Conclusions:

mosGraphGPT offers a powerful new approach for analyzing multi-omic data.
The model enhances understanding of complex cellular signaling in diseases.
This work facilitates advancements in precision medicine and drug discovery.