Gabriele Partel

DeepSCENIC uses transfer learning from sequence-to-function models to infer causal gene regulatory networks from single-cell multiome data.

This paper introduces MIMA, a deep learning framework for multi-omics integration and batch correction, which effectively removes batch effects and preserves biological information across modalities.

The paper evaluates computational methods for predicting cell type-specific enhancers in the mouse cortex using in vivo validation data and finds that open chromatin and sequence models improve prediction accuracy.

The paper applies spatial transcriptomics to the adult Drosophila brain and body to map mRNA locations and integrate with single-cell data.

The paper uses a de novo spatial modeling approach to identify novel cell populations in the developing human heart, revealing previously unreported molecular diversity.

This paper introduces a graph-based method for analyzing in situ sequencing data to identify spatial compartments in mouse brain tissue, linking them to known brain regions and morphology.

Spage2vec is an unsupervised method for analyzing spatial transcriptomic data, revealing gene expression patterns in mouse brain tissue without prior segmentation or biological knowledge.