Author

Ioana Bica

University of CambridgeORCID 0000-0001-5564-0214Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Bioinformatics· 2

Signature Assays, Organisms & Methods

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Research Focus:clustering· 2 Approach:graph neural network· 2 Assay:scRNA-seq· 2 Research Focus:cell type annotation· 1 Research Focus:differential expression· 1 Research Focus:dimensionality reduction· 1 Organism:multiple species· 1 Approach:variational autoencoder· 1

Frequent Co-Authors

Helena Andrés-TerréiD· 2 shared Píetro LióiD· 2 shared Ana CvejiciD· 1 shared David ButereziD· 1 shared Ifrah TariqiD· 1 shared

Activity by Year

2020-2021 · peak 1/yr

Co-Author Collaboration Network

5 top collaborators · 8 mutual co-author links

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2 shared papersBioinformatics

Helena Andrés-TerréORCID

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2 papers

Recent·Most cited

BioinformaticsBioinformaticsPeer-reviewedNov 24, 2021

CellVGAE: An unsupervised scRNA-seq analysis workflow with graph attention networks

David Buterez, Ioana Bica, Ifrah Tariq…·Píetro Lió

CellVGAE uses graph attention networks for scRNA-seq analysis, improving dimensionality reduction and clustering across multiple datasets.

44 citations

scRNA-seqgraph neural network

𝕏 PostBluesky

BioinformaticsScientific ReportsPeer-reviewedJun 17, 2020

Unsupervised generative and graph representation learning for modelling cell differentiation

Ioana Bica, Helena Andrés-Terré, Ana Cvejic, Píetro Lió

This paper develops a computational framework using variational autoencoders and graph autoencoders to model cell differentiation from single-cell RNA-seq data across species.

20 citations

multiple speciesscRNA-seq

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