J Integr Bioinform - Noise tolerance of multiple classifier systems in data integration-based gene function prediction.

Tópicos

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Resumo

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. In this contribution we investigated the tolerance of multiple classifier systems (MCS) to noisy data in gene function prediction experiments based on data integration methods. The experimental results show that performances of MCS do not undergo a significant decay when noisy data sets are added. In addition, we show that in this task MCS are competitive with kernel fusion, one of the most widely applied technique for data integration in gene function prediction problems.

Resumo Limpo

avail various highthroughput experiment comput method develop last decad allow molecular biologist investig function gene system level open unpreced research opportun despit autom predict gene function includ difficult problem bioinformat sever recent publish work show consist improv predict perform can obtain integr heterogen data sourc nevertheless work dedic investig impact noisi data predict perform achiev use data integr approach contribut investig toler multipl classifi system mcs noisi data gene function predict experi base data integr method experiment result show perform mcs undergo signific decay noisi data set ad addit show task mcs competit kernel fusion one wide appli techniqu data integr gene function predict problem

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