Author

Ouyang Zhu

University of Notre Dame

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· 3 Genomics· 1

Signature Assays, Organisms & Methods

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Research Focus:benchmarking· 2 Assay:bulk RNA-seq· 2 Approach:deep learning· 2 Research Focus:perturbation response prediction· 2 Research Focus:cell type annotation· 1 Assay:CITE-seq· 1 Approach:large language model· 1 Approach:machine learning (classical)· 1

Frequent Co-Authors

Jun LiiD· 2 shared Jun LiiD· 1 shared Junwei SuniD· 1 shared Yiqun T. CheniD· 1 shared Yuxun He· 1 shared

Activity by Year

2024-2026 · peak 2/yr

Co-Author Collaboration Network

5 top collaborators · 3 mutual co-author links

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2 shared papersBioinformatics

Jun LiORCID

Author profile →

4 papers

Recent·Most cited

A structured study of cross-condition prediction of transcriptional responses to gene perturbations

Ouyang Zhu, Jun Li

This study introduces TranScouter, a framework for predicting transcriptional responses to gene perturbations across different biological conditions, showing competitive performance in both seen and unseen perturbation scenarios.

bioRxiv · Aug 5, 2026 · PreprintBioinformatics

Beyond Expression Prediction: Benchmarking Differential Expression Classification in Single-Cell Perturbation Models

Junwei Sun, Yuxun He, Ouyang Zhu, Yiqun T. Chen

This paper benchmarks differential expression classification in single-cell perturbation models, finding that simple baselines often outperform deep learning models.

bioRxiv · Jul 24, 2026 · PreprintGenomics

scPDA: Denoising Protein Expression in Droplet-Based Single-Cell Data

Ouyang Zhu, Jun Li

scPDA denoises protein expression in single-cell droplet data, improving cell-type identification by removing noise more thoroughly while preserving biological signals.

Genome biology · Jul 17, 2025Peer-reviewedBioinformatics

Scouter: Predicting Transcriptional Responses to Genetic Perturbations with LLM embeddings

Ouyang Zhu, Jun Li

Scouter predicts transcriptional responses to genetic perturbations using LLM embeddings, achieving higher accuracy than existing methods.

bioRxiv · Dec 6, 2024 · PreprintBioinformatics