Alessandro Petrini

Post-doc Researcher

PhD: 2021; PhD in Computer Science; Università degli Studi di Milano

MSc: 2017; M.Sc. in Computer Science; Università degli Studi di Milano

BSc: 2014; B.Sc. in Applied Mathematics; Università degli Studi di Milano

Email: alessandro (dot) petrini (at) unimi (dot) it

Main interests: High-Performance Computing (heterogeneous, accelerated, large scale), Machine Learning, Bioinformatics

Alessandro Petrini received his PhD in computer science at Università degli Studi of Milan, Italy in early 2021. In 2014 he received a BSc in Applied Mathematics at Università degli Studi of Milan, Italy and in 2017 a MSc in computer Science at Università degli Studi of Milan, Italy. His main research activity is in the field of high-performance and Accelerated Computing with applications in Machine Learning and Bioinformatics by developing highly scalable and efficient software solutions targeted to GPU-accelerated and massive etherogeneous systems. He is also currently interested in biomedical image processing. In the past he has worked on the topics of video compression and encoding, optimization and virtualization of network functions, network compression.

Publications

L. Cappelletti, J. Gliozzo, A. Petrini and G. Valentini. Training Neural Networks with Balanced Mini-batch to Improve the Prediction of Pathogenic Genomic Variants in Mendelian Diseases. Sensors & Transducers, IFSA Publishing, SL 234(6), 2019.

A. Petrini, M. Schubach, M. Re, M. Frasca, M. Mesiti, G. Grossi, T. Castrignanò, P. Robinson and G. Valentini. Parameters tuning boosts HyperSMURF predictions of rare deleterious non-coding genetic variants. PeerJ Preprints, PeerJ Inc. San Francisco, USA 5, 2017.

J. Gliozzo, P. Perlasca, M. Mesiti, E. Casiraghi, V. Vallacchi, E. Vergani, M. Frasca, G. Grossi, A. Petrini, M. Re, A. Paccanaro and G. Valentini. Network modeling of patients’ biomolecular profiles for clinical phenotype/outcome prediction. Scientific Reports, Nature Publishing, 2020.

P. Perlasca, M. Frasca, C. Ba, M. Notaro, A. Petrini, E. Casiraghi, G. Grossi, J. Gliozzo, G. Valentini and M. Mesiti. UNIPred-Web: a web tool for the integration and visualization of biomolecular networks for protein function prediction. BMC Bioinformatics, BioMed Central 20(1), 2019.

M. Frasca, G. Grossi, J. Gliozzo, M. Mesiti, M. Notaro, P. Perlasca, A. Petrini and G. Valentini. A GPU-based algorithm for fast node label learning in large and unbalanced biomolecular networks. BMC Bioinformatics, BioMed Central 19(10), 2018.

N. Zhou, Y. Jiang, [...], M. Frasca, M. Notaro, G. Grossi, A. Petrini, M. Re, G. Valentini, M. Mesiti and others. The CAFA challenge reports improved protein function prediction and new functional annotations for hundreds of genes through experimental screens. Genome Biology 20(1), 2019.

C. Ba, E. Casiraghi, M. Frasca, J. Gliozzo, G. Grossi, M. Mesiti, M. Notaro, P. Perlasca, A. Petrini, M. Re and Others. A Graphical Tool for the Exploration and Visual Analysis of Biomolecular Networks. International Meeting on Computational Intelligence Methods for Bioinformatics and Biostatistics, 2018.

J. Gliozzo, P. Perlasca, M. Mesiti, E. Casiraghi, V. Vallacchi, E. Vergani, M. Frasca, G. Grossi, A. Petrini, M. Re and Others. Network modeling of patients' biomolecular profiles for clinical phenotype/outcome prediction. Scientific reports, Nature Publishing Group 10(1), 2020.

L. Cappelletti, A. Petrini, J. Gliozzo, E. Casiraghi, M. Schubach, M. Kircher and G. Valentini. Bayesian optimization improves tissue-specific prediction of active regulatory regions with deep neural networks. International Work-Conference on Bioinformatics and Biomedical Engineering, 2020.

A. Petrini, M. Mesiti, M. Schubach, M. Frasca, D. Danis, M. Re, G. Grossi, L. Cappelletti, T. Castrignanò, P. Robinson and Others. parSMURF, a high-performance computing tool for the genome-wide detection of pathogenic variants. GigaScience, Oxford University Press 9(5), 2020.

M. Frasca, M. Sepehri, A. Petrini, G. Grossi and G. Valentini. Committee-Based Active Learning to Select Negative Examples for Predicting Protein Functions. International Meeting on Computational Intelligence Methods for Bioinformatics and Biostatistics, 2018.