Author

Geoffrey F. Schau

Oregon Health & Science UniversityORCID 0009-0000-9331-0638Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

Click to filter author papers

Bioinformatics· 1 Neuroscience· 1 Systems Biology· 1

Signature Assays, Organisms & Methods

Click to filter · or open tag

Approach:deep learning· 2 Assay:ATAC-seq· 1 Assay:bulk RNA-seq· 1 Research Focus:cell type annotation· 1 Research Focus:clustering· 1 Research Focus:gene regulatory network inference· 1 Organism:human· 1 Assay:imaging· 1

Frequent Co-Authors

Erik A. BurlingameiD· 2 shared Young Hwan ChangiD· 2 shared Aaron D. GarciaiD· 1 shared Abhejit RajagopaliD· 1 shared Agata BudzilloiD· 1 shared Alex TraniD· 1 shared Alexander ViethiD· 1 shared Alida AvolaiD· 1 shared

Activity by Year

2020-2025 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 29 mutual co-author links

Click any collaborator node to inspect or filter shared papers

Focal author (center)Selected co-authorORCID co-authorUnlinked name

Curved chords = mutual co-authorship

2 shared papersBioinformatics

Erik A. BurlingameORCID

Mutual co-authors in cluster

Author profile →

3 papers

Recent·Most cited

Cross-species consensus atlas of the primate basal ganglia

Nelson Johansen, Yuanyuan Fu, Matthew T. Schmitz, Alma Dubuc, Niklas Kempynck, Morgan E. Wirthlin, Aaron D. Garcia, Madeleine N. Hewitt, Meghan A. Turner, Stephanie C. Seeman, Brian Long, Xiao-Ping Liu, Shu Dan, Michael DeBerardine, Inkar Kapen, , Trygve E. Bakken

The paper creates a cross-species consensus atlas of primate basal ganglia cell types using multiomic data from human, macaque, and marmoset, identifying 61 conserved cell types and a novel hybrid medium spiny neuron type.

bioRxiv · Dec 16, 2025 · PreprintNeuroscience

DISSECT: DISentangle SharablE ConTent for Multimodal Integration and Crosswise-mapping

Geoffrey F. Schau, Erik A. Burlingame, Young Hwan Chang

The paper introduces a method to separate domain-specific information using a cross-domain autoencoder and orthogonal gate block for improved domain translation.

IEEE Conference on Decision and Control (CDC) · Dec 14, 2020Peer-reviewedBioinformatics

SHIFT: speedy histological-to-immunofluorescent translation of whole slide images enabled by deep learning

Erik A. Burlingame, Mary McDonnell, Geoffrey F. Schau, Guillaume Thibault, Christian Lanciault, Terry K. Morgan, Brett Johnson, Christopher L. Corless, Joe W. Gray, Young Hwan Chang

This paper introduces a deep learning method called SHIFT that translates histological images into virtual immunofluorescence images, improving efficiency and reducing costs in cancer diagnostics.

Scientific Reports · Oct 15, 2020Peer-reviewedSystems Biology