Network biology and Network Science in general is a novel field in the intersection of computer science, mathematics and biology. While Network Biology is critical for understanding of cells, Network Science is critical for understanding complex systems.

Nowadays rapid changes are happening, so there is the need to develop and new computational/algorithmic methods for novel challenges have arisen.

Selected Publications.

Survey of local and global biological network alignment: the need to reconcile the two sides of the same coin

PH Guzzi, T Milenković

Briefings in bioinformatics 19 (3), 472-481

Modeling multi-scale data via a network of networks

S Gu, M Jiang, PH Guzzi, T Milenković

Bioinformatics 38 (9), 2544-2553

Editorial Deep Learning and Graph Embeddings for Network Biology

PH Guzzi, M Zitnik

IEEE/ACM Transactions on Computational Biology and Bioinformatics 19 (2 …

A novel algorithm for finding top-k weighted overlapping densest connected subgraphs in dual networks

R Dondi, MM Hosseinzadeh, PH Guzzi

Applied Network Science 6 (1), 1-17

Protein-to-protein interactions: Technologies, databases, and algorithms

M Cannataro, PH Guzzi, P Veltri

ACM Computing Surveys (CSUR) 43 (1), 1-36

COVID-19 Data Analysis

Data science in unveiling covid-19 pathogenesis and diagnosis: evolutionary origin to drug repurposing

JK Das, G Tradigo, P Veltri, PH Guzzi, S Roy

Briefings in Bioinformatics

Exploiting the molecular basis of age and gender differences in outcomes of SARS-CoV-2 infections

D Mercatelli, E Pedace, P Veltri, FM Giorgi, PH Guzzi

Computational and Structural Biotechnology Journal 19, 4092-4100

Master regulator analysis of the SARS-CoV-2/human interactome

PH Guzzi, D Mercatelli, C Ceraolo, FM Giorgi

Journal of clinical medicine 9 (4), 982

Past Projects in Data Analysis

Analysis and Management of protein interaction networks.

Interactions among proteins constitute an important aspect of the study of such molecules. In such field the bioinformatic community support the molecular biology by providing efficient solution for the management and analysis of data. Pietro initially studied deeply existing approaches and challenges (Cannataro et al ACM Computing Surveys 2010), then he proposed some novel approaches. IMPRECO (Improving the Prediction of Protein Complexes), is a protein complex metapredictor the analyze protein interaction networks integrating results of existing predictors in a distributed envirornment (FUTURE GENERATION COMPUTER SYSTEMS, 2010).

After such experiences, Pietro focused on the comparison of protein interaction networks, developing some novel local alignment algorithms. A local alignment algorithm finds region of similarity among networks employing ad hoc heuristics to avoid the complexity of the sub-graph isomorphis problem. In such field in collaboration with the Padova University and the GeorgiaTech, Pietro contributed to the definition of (AlignNeMo) a novel alignemtn algorithm based on a novel approach that integrate both homology and topology (Ciriello et al., PLoSOne-2012). Successively, Pietro lead the design of Align-MCL an improvement of AlignNemo (Mina and Guzzi, IEEE/ACM Transactions on Computational Biology and Bioinformatics). Align-MCL has been demonstrated to outperform existing alignemtn approaches (Faizal et al., Eurasip 2015).

Actually Pietro is developing a novel approach of the analysis based on the integration of both local and global alignment approaches (Guzzi and Milenkovic Brief Bioinfo 2017) and to use network alignment to study brain networks (Milano et al., Bmc Bioinfo 2016) in collaboration with UCSD.

Compenteces in this field are also evidenced in publications made in collaboration with medical research units (Nassa et al., MolBiosyst-2010), (Nassa et al., Proteomics-2011.

Pietro is also co-author of the book: "Data Management of Protein Interaction Networks", Wiley-IEEE Computer..

Prototipi:

· AlignNemo: a software for local alignment of protein interaction (a collaboration with Padova University );

· Align-MCL: a software for local alignemnt of protein interaction networks;

· cytoMCL e cytoSevis: two Cytoscape plug-ins for the clustering and semantic visualization of protein interaction networks;

· IMPRECO: a protein complex meta-predictiors based on the integration of existing predictors;

· MODULA: a software for local alignment of protein interaction networks (in collaboration with Northern Hill University and Colorado University);

· M-FINDER: a software for the discovery of functionally related proteins in protein interaction networks based on Gene Ontology and a information propagation algorithm (in collaboration with Baylor);

· SSN-Analyzer: a software tool for denosing semantic similarity networks;

· VeNET : a software for the analysis of biological networks based on Vector Space Embedding.