A parallel implementation of the network identification by multiple regression (NIR) algorithm to reverse-engineer regulatory gene networks.
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 algo...
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2010-04-01
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| Series: | PLoS ONE |
| Online Access: | https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0010179&type=printable |
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| author | Francesco Gregoretti Vincenzo Belcastro Diego di Bernardo Gennaro Oliva |
| author_facet | Francesco Gregoretti Vincenzo Belcastro Diego di Bernardo Gennaro Oliva |
| author_sort | Francesco Gregoretti |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-c5eccb6ce2a0447e8bc8d7d723ca4bbd |
| institution | OA Journals |
| issn | 1932-6203 |
| language | English |
| publishDate | 2010-04-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj-art-c5eccb6ce2a0447e8bc8d7d723ca4bbd2025-08-20T02:01:57ZengPublic Library of Science (PLoS)PLoS ONE1932-62032010-04-0154e1017910.1371/journal.pone.0010179A parallel implementation of the network identification by multiple regression (NIR) algorithm to reverse-engineer regulatory gene networks.Francesco GregorettiVincenzo BelcastroDiego di BernardoGennaro OlivaThe 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.https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0010179&type=printable |
| spellingShingle | Francesco Gregoretti Vincenzo Belcastro Diego di Bernardo Gennaro Oliva A parallel implementation of the network identification by multiple regression (NIR) algorithm to reverse-engineer regulatory gene networks. PLoS ONE |
| title | A parallel implementation of the network identification by multiple regression (NIR) algorithm to reverse-engineer regulatory gene networks. |
| title_full | A parallel implementation of the network identification by multiple regression (NIR) algorithm to reverse-engineer regulatory gene networks. |
| title_fullStr | A parallel implementation of the network identification by multiple regression (NIR) algorithm to reverse-engineer regulatory gene networks. |
| title_full_unstemmed | A parallel implementation of the network identification by multiple regression (NIR) algorithm to reverse-engineer regulatory gene networks. |
| title_short | A parallel implementation of the network identification by multiple regression (NIR) algorithm to reverse-engineer regulatory gene networks. |
| title_sort | parallel implementation of the network identification by multiple regression nir algorithm to reverse engineer regulatory gene networks |
| url | https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0010179&type=printable |
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