Stefano Di Carlo

Multi-ancestry admixture mapping reveals ancestry-associated disease loci in the UK Biobank

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: a scverse-compatible package for multi-omic single-cell trajectory inference integration

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

On the state of protein function prediction: a report on the fourth CAFA challenge

This paper reports on the fourth CAFA challenge, showing improvements in Gene Ontology term prediction but highlighting challenges in new evaluation settings.

Spike Train Scalograms (STS): a Deep Learning Classification Pipeline for Neuronal Cell Types

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

Start&Stop - a PhysiCell and PhysiBoSS 2.0 add-on for interactive simulation control

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

Fast and Accurate LSTM Meta-modeling of TNF-induced Tumor Resistance In Vitro

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

Biology System Description Language (BiSDL): a modeling language for the design of multicellular synthetic biological systems

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

A starting guide on multi-omic single-cell data joint analysis: basic practices and results

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

Cross-omic Transcription Factors meta-analysis: an insight on TFs accessibility and expression correlation

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

Computational Methods for Biofabrication in Tissue Engineering and Regenerative Medicine - a literature review

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

GRAIGH: Gene Regulation accessibility integrating GeneHancer database

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

A methodology combining reinforcement learning and simulation to optimize the in silico culture of epithelial sheets.

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

Meta-analysis of gene activity (MAGA) contributions and correlation with gene expression, through GAGAM.

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.

GAGAM: a genomic annotation-based enrichment of scATAC-seq data for Gene Activity Matrix

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.

A methodology for co-simulation-based optimization of biofabrication protocols

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

Meta-Analysis of cortical inhibitory interneurons markers landscape and their performances in scRNA-seq studies.

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

Nets-within-nets for modeling emergent patterns in ontogeneticprocesses

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