Research Activities

Post date: 6-feb-2012 20.49.55

Systematic Analysis of Protein Interaction Networks

Prediction of Protein Complexes.

Proteins interact among them and different interactions are represented as graphs named Protein to Protein Interaction (PPI) networks. The interest in analyzing PPI networks is related to the possibility of predicting PPI properties on the basis of global properties of the graph (e.g. verify if homology among species involves PPI similarity), or to find set of protein interactions that have a biological meaning. Evolutionary analysis and comparison of biological networks may result in the identification of conserved mechanism between species as well as conserved modules, such as protein complexes and pathways.

M Mina and Guzzi PH Align-MCL: Local Alignment of Protein Interaction Networks through Markov Clustering. Proceedings of IEEE BIBMW. Submitted to IEEE TCBB https://sites.google.com/site/alignmcl/

Ciriello G, Mina M, Guzzi PH, Cannataro M, Guerra C (2012) AlignNemo: A Local Network Alignment Method to Integrate Homology and Topology. PLoS ONE 7(6): e38107. doi:10.1371/journal.pone.0038107

CytoMCL: markov clustering of biological networks in Cytoscape. It is a plugin for the Cytoscape Platform implementing the Markov Clustering (MCL) algorithm by Stjin Van Dongen. CytoscapePlugin HomePage

M Cannataro, PH Guzzi, P Veltri IMPRECO: Distributed Prediction of Protein Complexes Future Generation Computer Systems, aFuture Generation Computer Systems FGCS 10.1016/j.future.2009.08.001

M Cannataro, PH Guzzi, P Veltri: myMCL: a web portal for Protein Complex Prediction, accepted to: 21th IEEE International Symposium on Computer-Based Medical Systems (CBMS 2008 June 17-19, 2008 at University of Jyväskylä, Project Home Page myMCL: a Web Portal Interface to MCL algorithm.

Giuseppe Agapito, Pietro Hiram Guzzi, Mario Cannataro: Visualization of protein interaction networks: problems and solutions. BMC Bioinformatics 14(S-1): S1 (2013)

IMPRECO: A Framework to improve the Prediction of Protein Complexes Project Home Page

Microarray Data Analysis

In this field i'm involved in a joint work between the Bioinformatics Laboratory and the Medical Oncology Unit and Referal Center for Genetic Counselling and Innovative treatments, T.Campanella Cancer Center, Catanzaro, Italy. My activities are: supporting the definition of a pipeline of analysis, collaborating to the definition of supporting tools.

Pietro Hiram Guzzi, Giuseppe Agapito, Maria Teresa Di Martino, Mariamena Arbitrio,Pierfrancesco Tassone, Pierosandro Tagliaferri, Mario Cannataro: DMET-Analyzer: automatic analysis of Affymetrix DMET Data. BMC Bioinformatics 13: 258 (2012)

M Cannataro, PH Guzzi mu-CS: An extension of the TM4 platform to manage Affymetrix binary data. accepted in BMC Bioinformatics 2010, 11:315 (10 June 2010)

Di Martino, MT, Ventura, M, Guzzi, PH, Pietragalla, A, Neri, P, Bulotta, A, Calimeri, T, Barbieri, V, Caraglia, M, Veltri, P, Cannataro, M, Tassone, P, Tagliaferri, P Differential transcriptional response to cisplatinum in BRCA1-defective versus BRCA1-reconstituted breast cancer cells by microarrays. Cancer Res 2009 69: 5062 Link

Semantic Based Analysis of Biological Data

Although the introduction of a large number of algorithms and methodologies that use ontologies (such as GO) for the analysis of biological data, there is the lack for a comprehensive study elucidating risks due to the bias and issues in such use. Recently i'm focusing on the investigation of such problems.

In the past i was involved in two different projects: the development of a partitioning strategy for biological ontologies and the use of ontologies for supporting a workflow editor. The increasing dimensions of themselves ( e.g. Gene Ontology) suggest the introduction of a methodology. I was involved in the development of a methodology for the partitioning and the distributed management of OWL ontologies. The rationale of the developed approach is the distinction between schema and instances.

Pietro Hiram Guzzi, Marco Mina, Concettina Guerra and Mario Cannataro: Semantic Similarity Measures: Assessment with biological features and Issues, to appear in in Briefings In Bioinformatics Oxford Journal 10.1093/bib/BBR066

GUZZI P., MINA M (in press). Investigating bias in semantic similarity measures for analysis of protein interactions. In: Proceedings of 1st International Workshop on Pattern Recognition in Proteomics, Structural Biology and Bioinformatics (PR PS BB 2011) published in Nuovo Cimento. Ravenna, 13th September 2011