Davide Maspero

Davide Maspero, Alex Graudenzi, Marco Antoniotti, 2020, 2020 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB).

Variant calling from scRNA-seq data allows the assessment of cellular identity in patient-derived cell lines

M. Antoniotti, A. Graudenzi, I. Castiglioni, 2021, Nature Communications.

Integration of transcriptomic data and metabolic networks in cancer samples reveals highly significant prognostic power

Giancarlo Mauri, Davide Maspero, Marco Antoniotti, 2018, J. Biomed. Informatics.

Early detection and improved genomic surveillance of SARS-CoV-2 variants from deep sequencing data

M. Antoniotti, A. Graudenzi, Davide Maspero, 2021, iScience.

VERSO: A comprehensive framework for the inference of robust phylogenies and the quantification of intra-host genomic diversity of viral samples

M. Antoniotti, A. Graudenzi, Davide Maspero, 2020, bioRxiv.

VirMutSig: Discovery and assignment of viral mutational signatures from sequencing data

A. Graudenzi, D. Porro, Davide Maspero, 2021, STAR Protocols.

Large-scale analysis of SARS-CoV-2 synonymous mutations reveals the adaptation to the human codon usage during the virus evolution

M. Antoniotti, A. Graudenzi, Mario Mauri, 2022, bioRxiv.

Large-scale analysis of synonymous viral variants reveals global adaptation of the SARS-CoV-2 to the human codon usage

M. Antoniotti, A. Graudenzi, Mario Mauri, 2021 .

Integration of single-cell RNA-seq data into metabolic models to characterize tumour cell populations

Giancarlo Mauri, Davide Maspero, Marzia Di Filippo, 2018, bioRxiv.

LACE 2.0: an interactive R tool for the inference and visualization of longitudinal cancer evolution

C. Damiani, M. Antoniotti, A. Graudenzi, 2023, BMC Bioinformatics.

LACE: Inference of cancer evolution models from longitudinal single-cell sequencing data

M. Antoniotti, A. Graudenzi, I. Castiglioni, 2021, J. Comput. Sci..

Longitudinal cancer evolution from single cells

Daniele Ramazzotti, Rocco Piazza, Davide Maspero, 2020, bioRxiv.

Read-through transcripts in lung: germline genetic regulation and correlation with the expression of other genes.

T. Dragani, L. De Cecco, M. Incarbone, 2020, Carcinogenesis.

Single-cell Digital Twins for Cancer Preclinical Investigation.

Giancarlo Mauri, Chiara Damiani, Davide Maspero, 2020, Methods in molecular biology.

Integration of single-cell RNA-seq data into population models to characterize cancer metabolism

Giancarlo Mauri, Hans V. Westerhoff, Chiara Damiani, 2019, PLoS Comput. Biol..

Combining multi-target regression deep neural networks and kinetic modeling to predict relative fluxes in reaction systems

Davide Maspero, Chiara Damiani, Alex Graudenzi, 2021, Inf. Comput..

Cigarette smoke alters the transcriptome of non-involved lung tissue in lung adenocarcinoma patients

T. Dragani, L. De Cecco, M. Dugo, 2019, Scientific Reports.

On the Use of Topological Features of Metabolic Networks for the Classification of Cancer Samples

Davide Maspero, Marco Antoniotti, Chiara Damiani, 2021, Current genomics.

An Optimal Control Framework for the Automated Design of Personalized Cancer Treatments

Davide Maspero, Alex Graudenzi, Marco Antoniotti, 2020, Frontiers in Bioengineering and Biotechnology.

Personalized Therapy Design for Liquid Tumors via Optimal Control Theory

Rocco Piazza, Alex Graudenzi, Marco Antoniotti, 2019, bioRxiv.

MaREA4Galaxy: Metabolic reaction enrichment analysis and visualization of RNA-seq data within Galaxy

Giancarlo Mauri, Chiara Damiani, Lorenzo Rovida, 2020, Computational and structural biotechnology journal.

LACE 2.0: an interactive R tool for the inference and visualization of longitudinal cancer evolution

C. Damiani, M. Antoniotti, Gianluca Ascolani, 2023, BMC Bioinformatics.