Publications

Pertpy is a Python-based modular framework for large-scale single-cell perturbation analysis, providing harmonized datasets, metadata annotation, and fast implementations of established and novel methods within the scverse ecosystem.

Nicheformer is a transformer-based foundation model for single-cell and spatial omics, pretrained on SpatialCorpus-110M, that learns spatially informed cell representations and enables transfer of spatial context to dissociated scRNA-seq data.

We present scConcept, a transformer-based contrastive learning framework for single-cell transcriptomics that learns technology-agnostic cell embeddings by contrasting multiple views of cells, outperforming foundation models and domain-specific methods across diverse downstream tasks.

Self-supervised learning (SSL) has emerged as a powerful method for extracting meaningful representations from vast, unlabelled datasets, transforming computer vision and natural language processing. …

We benchmark 16 distance metrics for single-cell RNA-seq and recommend mean squared error (MSE) for comparing populations and evaluating gene expression prediction models.

The need for a large amount of labeled data in the supervised setting has led recent studies to utilize self-supervised learning to pre-train deep neural networks using unlabeled data …

The increased amount of multi-modal medical data has opened the opportunities to simultaneously process various modalities such as imaging and non-imaging data to gain a comprehensive insight into …

Single-cell RNA-sequencing (scRNA-seq) offers the opportunity to dissect heterogeneous cellular compositions and interrogate the cell-type-specific gene expression patterns across diverse conditions. However …