Author

András Lakatos

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Open access Sep 2026

EL-PACA: ensembled low-dimensional projections for accurate cell-type annotation using deep learning for single-cell transcriptomics of human tissues and organoids

Single-cell transcriptomics has been revolutionising the discovery of human cell types and disease mechanisms. Accurate human cell-subtype annotation in complex neural tissues and disorders remains challenging due to the scarcity of postmortem tissue availability, in vitro culture-specific influences in surrogate m...

Muhammad Umar, András Lakatos, Arif Mahmood et al. · Scientific Reports · 0 citations

Open access Jul 2026

scRepresenter: a workflow for computing, integrating and benchmarking cellular representations in single-cell transcriptomics

Motivation Single-cell RNA sequencing (scRNA-seq) has become an attractive tool for studying complex diseases, in which transient cell states affecting diverse cell populations characterise disease development and progression. However, due to data sparsity and disease heterogeneity analysis is often challenging. With r...

Guilherme Pocas, Muhammad Umar, Oliver Davis et al. · bioRxiv · 0 citations