Author

Xubo B. Song

Oregon Health & Science UniversityORCID 0000-0002-5138-3983Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Bioinformatics· 3

Signature Assays, Organisms & Methods

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Assay:bulk RNA-seq· 1 Research Focus:cell type annotation· 1 Research Focus:drug response / drug discovery· 1 Approach:graph neural network· 1 Organism:human· 1 Assay:imaging· 1 Approach:machine learning (classical)· 1 Approach:statistical model· 1

Frequent Co-Authors

Guanming WuiD· 2 shared Christopher Z. Eddy· 1 shared Daguang XuiD· 1 shared Deidre BeaversiD· 1 shared Emek DemiriD· 1 shared GEORGE V. THOMASiD· 1 shared Gordon Brent MillsiD· 1 shared Kaoutar Ait-AhmadiD· 1 shared

Activity by Year

2024-2025 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 23 mutual co-author links

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2 shared papersBioinformatics

Guanming WuORCID

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Author profile →

3 papers

Recent·Most cited

MiroSCOPE: An AI-driven digital pathology platform for annotating functional tissue units

Madeleine R. Fenner, Selim Sevim, Guanming Wu, Deidre Beavers, Pengfei Guo, Yucheng Tang, Christopher Z. Eddy, Kaoutar Ait-Ahmad, Travis L Rice-Stitt, GEORGE V. THOMAS, Marigold Kuykendall, Vasilis Stavrinides, Mark Emberton, Daguang Xu, Xubo B. Song, , Emek Demir

MiroSCOPE is an AI platform for annotating functional tissue units in digital pathology, enabling efficient annotation of over 71,900 units across prostate cancer samples.

bioRxiv · Jul 11, 2025 · PreprintBioinformatics

Aggregating multimodal cancer data across unaligned embedding spaces maintains tumor of origin signal

Raphael Kirchgaessner, Kaya Keutler, Layaa Sivakumar, Xubo B. Song, Kyle Ellrott

This study aggregates multimodal cancer data across unaligned embedding spaces and finds that tumor of origin signal is preserved for learning tasks.

bioRxiv · May 14, 2025 · PreprintBioinformatics

Graph Structured Neural Networks for Perturbation Biology

Nathaniel J. Evans, Gordon Brent Mills, Guanming Wu, Xubo B. Song, Shannon K. McWeeney

This paper introduces Graph Structured Neural Networks (GSNN) for perturbation biology, showing they outperform baselines in predicting perturbed gene expression, drug combinations, and disease-specific drug prioritization.

bioRxiv · Feb 28, 2024 · PreprintBioinformatics