Noise tolerance of Multiple Classifier Systems in data integration-based gene function prediction

Noise tolerance of Multiple Classifier Systems in data integration-based gene function prediction

The availability of various high-throughput experimental and computational methods developed in the last decade allowed molecular biologists to investigate the functions of genes at system level opening unprecedented research opportunities. Despite the automated prediction of genes functions could b...

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Main Authors: Rè Matteo, Valentini Giorgio
Format: Article
Language:English
Published: De Gruyter 2010-12-01
Series:Journal of Integrative Bioinformatics
Online Access:https://doi.org/10.1515/jib-2010-139
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spelling doaj-f605f42b56874369a0ace474bffab4bc2021-09-06T19:40:31ZengDe GruyterJournal of Integrative Bioinformatics1613-45162010-12-017334636210.1515/jib-2010-139biecoll-jib-2010-139Noise tolerance of Multiple Classifier Systems in data integration-based gene function predictionRè Matteo0Valentini Giorgio1Dipartimento di Scienze dell’Informazione, Università degli Studi di Milano v. Comelico 39 Milano, http://www.dsi.unimi.it, ItalyDipartimento di Scienze dell’Informazione, Università degli Studi di Milano v. Comelico 39 Milano, http://www.dsi.unimi.it, ItalyThe availability of various high-throughput experimental and computational methods developed in the last decade allowed molecular biologists to investigate the functions of genes at system level opening unprecedented research opportunities. Despite the automated prediction of genes functions could be included in the most difficult problems in bioinformatics, several recently published works showed that consistent improvements in prediction performances can be obtained by integrating heterogeneous data sources. Nevertheless, very few works have been dedicated to the investigation of the impact of noisy data on the prediction performances achievable by using data integration approaches.https://doi.org/10.1515/jib-2010-139
collection DOAJ
language English
format Article
sources DOAJ
author Rè Matteo
Valentini Giorgio
spellingShingle Rè Matteo
Valentini Giorgio
Noise tolerance of Multiple Classifier Systems in data integration-based gene function prediction
Journal of Integrative Bioinformatics
author_facet Rè Matteo
Valentini Giorgio
author_sort Rè Matteo
title Noise tolerance of Multiple Classifier Systems in data integration-based gene function prediction
title_short Noise tolerance of Multiple Classifier Systems in data integration-based gene function prediction
title_full Noise tolerance of Multiple Classifier Systems in data integration-based gene function prediction
title_fullStr Noise tolerance of Multiple Classifier Systems in data integration-based gene function prediction
title_full_unstemmed Noise tolerance of Multiple Classifier Systems in data integration-based gene function prediction
title_sort noise tolerance of multiple classifier systems in data integration-based gene function prediction
publisher De Gruyter
series Journal of Integrative Bioinformatics
issn 1613-4516
publishDate 2010-12-01
description The availability of various high-throughput experimental and computational methods developed in the last decade allowed molecular biologists to investigate the functions of genes at system level opening unprecedented research opportunities. Despite the automated prediction of genes functions could be included in the most difficult problems in bioinformatics, several recently published works showed that consistent improvements in prediction performances can be obtained by integrating heterogeneous data sources. Nevertheless, very few works have been dedicated to the investigation of the impact of noisy data on the prediction performances achievable by using data integration approaches.
url https://doi.org/10.1515/jib-2010-139
work_keys_str_mv AT rematteo noisetoleranceofmultipleclassifiersystemsindataintegrationbasedgenefunctionprediction
AT valentinigiorgio noisetoleranceofmultipleclassifiersystemsindataintegrationbasedgenefunctionprediction
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