Michele Caselle

Integration of single-cell multi-omic data with graph-based topic modelling

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

The intrinsic dimension of gene expression during cell differentiation

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.

Exploring the latent space of transcriptomic data withtopic modeling

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.

Topic Modeling analysis of the Allen Human Brain Atlas

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.

Ohno-miRNAs: intragenic miRNA pairs derived from whole-genome duplication

The paper identifies Ohno-miRNAs, intragenic miRNA pairs from whole-genome duplication, and shows they have higher retention rates, sequence similarity, and regulate common targets compared to SSD miRNAs.

Identification of interpretable clusters and associated signatures in breast cancer single cell data: a topic modeling approach

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.

Out-of-equilibrium gene expression fluctuations in presence of extrinsic noise

The paper analyzes how extrinsic noise affects protein concentration variability during gene activation or inactivation using a stochastic model and simulations.

Emergent Statistical Laws in Single-Cell Transcriptomic Data

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.

A 3D transcriptomics atlas of the mouse olfactory mucosa

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.

The impact of whole genome duplications on the human gene regulatory networks

This study examines how two rounds of whole genome duplication in vertebrates shaped human gene regulatory networks, showing increased complexity and redundancy from WGD events.

Genome-wide dynamics of RNA synthesis, processing and degradation without RNA metabolic labeling

The paper introduces INSPEcT-, a computational method to infer RNA synthesis, processing, and degradation dynamics from total RNA data without metabolic labeling, and applies it to study post-transcriptional regulation in various conditions.

Molecular Inverse Comorbidity between Alzheimer’s disease and Lung Cancer: new insights from Matrix Factorization

The paper uses matrix factorization to analyze molecular mechanisms behind the inverse comorbidity between Alzheimer’s disease and lung cancer, identifying new candidate factors and subtype-specific mechanisms.

Identification of microRNA clusters cooperatively acting on Epithelial to Mesenchymal Transition in Triple Negative Breast Cancer

The paper identifies miRNA clusters driving triple negative breast cancer subtypes using a new computational pipeline and validates their role in promoting the phenotype through proliferation and EMT control.