Alessandro Savino

This study uses multi-ancestry admixture mapping in the UK Biobank to identify ancestry-associated disease loci, finding 13 genome-wide significant associations and four novel risk loci.

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

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 study applies reinforcement learning to optimize synthetic biological networks by controlling Hopf bifurcations in ODE-based systems, showing its ability to identify bifurcation onset while facing challenges in persistent oscillations and computational speed.

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.