A Parallel Implementation of the Network Identification by Multiple Regression (NIR) Algorithm to Reverse-Engineer Regulatory Gene Networks (Articolo in rivista)

Type

• Prodotto della ricerca (Classe)
• Articolo in rivista (Classe)

Label

• A Parallel Implementation of the Network Identification by Multiple Regression (NIR) Algorithm to Reverse-Engineer Regulatory Gene Networks (Articolo in rivista) (literal)

Anno

• 2010-01-01T00:00:00+01:00 (literal)

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

• 10.1371/journal.pone.0010179 (literal)

Alternative label

• Francesco Gregoretti, Vincenzo Belcastro, Diego di Bernardo, Gennaro Oliva (2010)
  A Parallel Implementation of the Network Identification by Multiple Regression (NIR) Algorithm to Reverse-Engineer Regulatory Gene Networks
  in PloS one
  (literal)

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

• Francesco Gregoretti, Vincenzo Belcastro, Diego di Bernardo, Gennaro Oliva (literal)

Pagina inizio

• 0 (literal)

Pagina fine

• 0 (literal)

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

• 5-0 (literal)

Rivista

• PloS one (Rivista)

Note

• ISI Web of Science (WOS) (literal)

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

• Institute of High Performance Computing and Networking, Naples, Italy Telethon Institute of Genetics and Medicine, Naples, Italy Telethon Institute of Genetics and Medicine, Naples, Italy Institute of High Performance Computing and Networking, Naples, Italy (literal)

Titolo

• A Parallel Implementation of the Network Identification by Multiple Regression (NIR) Algorithm to Reverse-Engineer Regulatory Gene Networks (literal)

Abstract

• The reverse engineering of gene regulatory networks using gene expression profile data has become crucial to gain novel biological knowledge. Large amounts of data that need to be analyzed are currently being produced due to advances in microarray technologies. Using current reverse engineering algorithms to analyze large data sets can be very computational-intensive. These emerging computational requirements can be met using parallel computing techniques. It has been shown that the Network Identification by multiple Regression (NIR) algorithm performs better than the other ready-to-use reverse engineering software. However it cannot be used with large networks with thousands of nodes - as is the case in biological networks - due to the high time and space complexity. In this work we overcome this limitation by designing and developing a parallel version of the NIR algorithm. The new implementation of the algorithm reaches a very good accuracy even for large gene networks, improving our understanding of the gene regulatory networks that is crucial for a wide range of biomedical applications. (literal)

Prodotto di

• Metodi, algoritmi e strumenti software innovativi per le Scienze Computazionali (ICT.P09.002.001) (Modulo)
• Institute for high performance computing and networking (ICAR) (Istituto)

Autore CNR

• FRANCESCO GREGORETTI (Unità di personale interno)
• GENNARO OLIVA (Persona)

Incoming links:

Prodotto

• Institute for high performance computing and networking (ICAR) (Istituto)
• Metodi, algoritmi e strumenti software innovativi per le Scienze Computazionali (ICT.P09.002.001) (Modulo)

Autore CNR di

• GENNARO OLIVA (Persona)
• FRANCESCO GREGORETTI (Unità di personale interno)

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

• PloS one (Rivista)