Roberta Bardini

ATLAS integrates single-cell RNA-seq and ATAC-seq data to improve trajectory inference by jointly analyzing transcriptomic and chromatin accessibility information.

This paper integrates GeneHancer annotations with single-cell chromatin accessibility data to identify high-confidence enhancer-gene interactions specific to a human PBMC dataset.

The paper introduces a deep learning pipeline using wavelet transforms to classify neuronal cell types from electrophysiological data with high accuracy.

Start&Stop enables interactive simulation control in PhysiCell and PhysiBoSS 2.0 by allowing parameter adjustments during simulations.

The paper introduces an LSTM-based meta-model for simulating TNF-induced tumor resistance, achieving high accuracy and speed compared to traditional simulations.

The paper introduces BiSDL, a modeling language for multicellular synthetic biology, enabling spatial and multi-level cellular dynamics representation and simulation.

This paper presents a bioinformatic workflow for analyzing transcriptomic and epigenomic single-cell data separately and jointly to better understand cellular heterogeneity.

This paper investigates the correlation between transcription factor expression and motif information in functional genomic regions to understand TF dynamics across cell types.

This paper reviews computational methods for biofabrication in tissue engineering and regenerative medicine, highlighting gaps in multi-stage and multi-component modeling.

GRAIGH integrates GeneHancer database with scATAC-seq data to interpret chromatin accessibility and detect cellular heterogeneity by identifying cell-type-specific enhancer-gene associations.

This paper introduces NSS, a method to analyze neuronal excitability states using AP waveforms, and validates it on murine cortical neurons, identifying voltage-gated K+ channels as markers of excitability states.

This paper proposes a reinforcement learning-based method to optimize in silico protocols for simulating epithelial sheet fabrication.

This paper introduces a meta-analysis framework to study the contributions of the Gene Activity Matrix and its correlation with gene expression using the GAGAM model.

This paper introduces GAGAM, a method to enhance scATAC-seq data interpretation by linking peaks to genes through genomic annotations, improving interpretability over traditional Gene Activity Matrices.

This paper introduces a co-simulation-based optimization methodology for biofabrication protocols, demonstrating its feasibility in generating epithelial cell monolayer fabrication protocols.

The paper evaluates the performance of inhibitory interneuron markers in scRNA-seq datasets and finds that marker quality correlates with dataset reliability.

This paper introduces a Nets-Within-Nets formalism to model ontogenesis, demonstrating its ability to simulate developmental processes in C. elegans Vulval Precursor Cells.