Multi-functional Protein Clustering in PPI Networks (Contributo in atti di convegno)

Type

• Prodotto della ricerca (Classe)
• Contributo in atti di convegno (Classe)

Label

• Multi-functional Protein Clustering in PPI Networks (Contributo in atti di convegno) (literal)

Anno

• 2008-01-01T00:00:00+01:00 (literal)

Alternative label

• Pizzuti Clara, Rombo Simona (2008)
  Multi-functional Protein Clustering in PPI Networks
  in Proc. of the 2nd International Conference on Bioinformatics Research and Development - BIRD’08, Vienna, 7-9 Luglio 2008
  (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori

• Pizzuti Clara, Rombo Simona (literal)

Pagina inizio

• 318 (literal)

Pagina fine

• 330 (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#volumeInCollana

• 13 (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni

• Istituto di calcolo e reti ad alte prestazioni Università della Calabria (literal)

Titolo

• Multi-functional Protein Clustering in PPI Networks (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#isbn

• 978-3-540-70598-7 (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#curatoriVolume

• Mourad Elloumi; Josef Küng; Michal Linial; Robert F. Murphy; Kristan Schneider;Cristian Toma (Eds.) (literal)

Abstract

• Protein-Protein Interaction (PPI) networks contain valuable information for the isolation of groups of proteins that participate in the same biological function. Many proteins play different roles in the cell by taking part in several processes, but isolating the different processes in which a protein is involved is often a difficult task. In this paper we present a method based on a greedy local search technique to detect functional modules in PPI graphs. The approach is conceived as a generalization of the algorithm PINCoC to generate overlapping clusters of the interaction graph in input. Due to this peculiarity, multi-facets proteins are allowed to belong to different groups corresponding to different biological processes. A comparison of the results obtained by our method with those of other well known clustering algorithms shows the capability of our approach to detect different and meaningful functional modules. (literal)

Prodotto di

• Gestire, Estrarre Conoscenza e Ragionare sui Dati (ICT.P08.002.001) (Modulo)
• Institute for high performance computing and networking (ICAR) (Istituto)

Autore CNR

• CLARA PIZZUTI (Persona)

Incoming links:

Prodotto

• Institute for high performance computing and networking (ICAR) (Istituto)
• Gestire, Estrarre Conoscenza e Ragionare sui Dati (ICT.P08.002.001) (Modulo)

Autore CNR di

• CLARA PIZZUTI (Persona)