Author

Chau Do

Aalto University

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 Neuroscience· 1

Signature Assays, Organisms & Methods

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Assay:scRNA-seq· 2 Approach:contrastive learning· 1 Approach:deep learning· 1 Research Focus:differential expression· 1 Approach:experimental assay· 1 Organism:mouse· 1 Approach:optimal transport· 1 Research Focus:perturbation response prediction· 1

Frequent Co-Authors

Carolina Caban Rivera· 1 shared Chen LiiD· 1 shared Ellen M. UnterwaldiD· 1 shared George M. SmithiD· 1 shared H Lähdesmäki· 1 shared Harri J LähdesmäkiiD· 1 shared Justin Shinkle· 1 shared Lynn G. KirbyiD· 1 shared

Activity by Year

2025-2026 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 28 mutual co-author links

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1 shared paperNeuroscience

Carolina Caban Rivera

Mutual co-authors in cluster

Author profile →

3 papers

Recent·Most cited

Predicting single-cell responses to novel genetic perturbations with optimal transport

Chau Do, H Lähdesmäki

This paper introduces an optimal transport-based method to predict single-cell transcriptional responses to novel genetic perturbations, showing competitive performance on benchmark datasets.

bioRxiv · Jan 8, 2026 · PreprintBioinformatics

The ventral hippocampus and nucleus accumbens as neural substrates for cocaine contextual memory reconsolidation

Carolina Caban Rivera, Rachael Price, Ricardo Petrilli Fortuna, Chen Li, Chau Do, Justin Shinkle, Marco Ghilotti, Xiangdang Shi, Lynn G. Kirby, George M. Smith, Ellen M. Unterwald

This study investigates the role of the ventral hippocampus and nucleus accumbens in cocaine contextual memory reconsolidation and finds that disrupting these circuits reduces drug-seeking behavior.

Translational Psychiatry · Nov 21, 2025Peer-reviewedNeuroscience

Incorporating Hierarchical Information into Multiple Instance Learning for Patient Phenotype Prediction with scRNA-seq Data

Chau Do, Harri J Lähdesmäki

This paper improves patient phenotype prediction using scRNA-seq data by incorporating hierarchical cell type information into multiple instance learning, achieving better performance and interpretability.

bioRxiv · Feb 10, 2025 · PreprintBioinformatics