Filippo Valle

This paper introduces a graph-based topic modelling method for single-cell multi-omic data, showing improved performance in clustering and biological interpretation.

This study applies intrinsic dimension analysis to single-cell transcriptomic data to capture expression trajectories and define a cell potency score across species and differentiation processes.

This paper compares topic modeling methods on transcriptomic data to identify tissue structure and distinguish healthy from cancerous tissues, showing their effectiveness as embedding spaces for classification.

This paper introduces a hierarchical Stochastic Block Modeling approach to identify universal gene expression patterns in the human brain using the Allen Human Brain Atlas dataset.

This study uses topic modeling to cluster breast cancer single-cell data and identify gene signatures, improving interpretability and accuracy in distinguishing drug-sensitive and resistant cell types.

The paper identifies emergent statistical laws in single-cell transcriptomic data that mirror patterns in linguistics, ecology, and genomics, providing a framework to distinguish biological variability from statistical effects.

This paper creates a 3D spatial transcriptomics atlas of the mouse olfactory mucosa, revealing spatial gene distribution patterns and supporting the chromatographic theory of olfaction.