Author

Dekang Cao

Washington University in St. Louis

Listed by name: these papers carry no ORCID for this author, so different people with the same name may be mixed together.

Biology Disciplines

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Bioinformatics· 2 Systems Biology· 1

Signature Assays, Organisms & Methods

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Research Focus:gene regulatory network inference· 2 Organism:human· 2 Assay:multiome· 2 Approach:deep learning· 1 Research Focus:differential expression· 1 Assay:DNA methylation· 1 Approach:graph neural network· 1 Assay:Hi-C· 1

Frequent Co-Authors

Fuhai LiiD· 3 shared Guangfu LiiD· 3 shared Michael A. ProvinceiD· 3 shared Philip PayneiD· 3 shared Emily Chen· 2 shared Tianma XuiD· 2 shared Ben Dizdar· 1 shared Carlos CruchagaiD· 1 shared

Activity by Year

2024-2024 · peak 3/yr

Co-Author Collaboration Network

10 top collaborators · 37 mutual co-author links

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3 shared papersBioinformatics

Fuhai LiORCID

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3 papers

Recent·Most cited

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

Heming Zhang, Dekang Cao, Tianma Xu, Emily Chen, Guangfu Li, Yixin Chen, Philip Payne, Michael A. Province, Fuhai Li

This study introduces mosGraphFlow, a graph AI model for analyzing multi-omic data to identify AD-associated biomarkers and signaling pathways, achieving high classification accuracy.

bioRxiv · Aug 1, 2024 · PreprintBioinformatics

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

Heming Zhang, Di Huang, Emily Chen, Dekang Cao, Tianma Xu, Ben Dizdar, Guangfu Li, Yixin Chen, Philip Payne, Michael A. Province, Fuhai Li

mosGraphGPT is a foundation model for multi-omic signaling graphs trained on cancer data and fine-tuned for Alzheimer's Disease, improving classification accuracy and interpretability.

bioRxiv · Aug 1, 2024 · PreprintBioinformatics

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

Heming Zhang, Dekang Cao, Zirui Chen, Xiuyuan Zhang, Yixin Chen, Cole Sessions, Carlos Cruchaga, Philip Payne, Guangfu Li, Michael A. Province, Fuhai Li

mosGraphGen generates multi-omics signaling graphs to enable integrative and interpretable graph AI model development by converting multi-omics data into biologically meaningful graphs.

Bioinformatics Advances · Jan 1, 2024Peer-reviewedSystems Biology