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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
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Published on: October 11, 2018

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

Matteo Rè1, Giorgio Valentini

  • 1Dipartimento di Scienze dell'Informazione, Università degli Studi di Milano, Milano, Italy. ti.iminu@nuller.oettam

Journal of Integrative Bioinformatics
|April 9, 2010
PubMed
Summary
This summary is machine-generated.

Multiple classifier systems (MCS) show high tolerance to noisy data in gene function prediction. These systems perform competitively with kernel fusion, a popular data integration technique.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Systems Biology

Background:

  • High-throughput methods enable system-level gene function investigation.
  • Data integration improves gene function prediction accuracy.
  • The impact of noisy data on these predictions is understudied.

Purpose of the Study:

  • To investigate the tolerance of multiple classifier systems (MCS) to noisy data in gene function prediction.
  • To compare the performance of MCS with kernel fusion for data integration in this task.

Main Methods:

  • Utilized multiple classifier systems (MCS) for gene function prediction.
  • Employed data integration approaches with varying levels of noisy data.
  • Compared MCS performance against kernel fusion, a standard data integration technique.

Main Results:

  • MCS demonstrated robust performance with minimal decay even when noisy datasets were introduced.
  • MCS proved to be competitive with kernel fusion in gene function prediction accuracy.
  • The study highlights the resilience of MCS in the face of data imperfections.

Conclusions:

  • Multiple classifier systems are a viable and robust approach for gene function prediction, especially when dealing with imperfect data.
  • MCS offer a competitive alternative to established methods like kernel fusion for integrating heterogeneous data sources.
  • Further research into data integration strategies should consider the impact and management of noisy data.