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Ensemble Based Data Fusion for Gene Function Prediction

机译:基于集成的数据融合用于基因功能预测

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摘要

The availability of an ever increasing amount of data sources due to recent advances in high throughput biotechnologies opens unprecedented opportunities for genome-wide gene function prediction. Several approaches to integrate heterogeneous sources of biomolecular data have been proposed in literature, but they suffer of drawbacks and limitations that we could in principle overcome by applying multiple classifier systems. In this work we evaluated the performances of three basic ensemble methods to integrate six different sources of high-dimensional biomolecular data. We also studied the performances resulting from the application of a simple greedy classifier selection scheme, and we finally repeated the entire experiment by introducing a feature filtering step. The experimental results show that data fusion realized by means of ensemble-based systems is a valuable research line for gene function prediction.
机译:由于高通量生物技术的最新进展,越来越多的数据源的可用性为全基因组范围的基因功能预测提供了前所未有的机会。文献中已经提出了几种整合生物分子数据异构源的方法,但是它们存在一些缺点和局限性,我们原则上可以通过应用多个分类器系统来克服这些缺点和局限性。在这项工作中,我们评估了三种基本集成方法的性能,这些方法集成了六种不同的高维生物分子数据来源。我们还研究了应用简单贪婪分类器选择方案所产生的性能,最后通过引入特征过滤步骤重复了整个实验。实验结果表明,基于集成系统的数据融合是基因功能预测的重要研究方向。

著录项

  • 来源
    《Multiple classifier systems》|2009年|448-457|共10页
  • 会议地点 Reykjavik(IS);Reykjavik(IS)
  • 作者

    Matteo Re; Giorgio Valentini;

  • 作者单位

    DSI, Dipartimento di Scienze dell' Informazione, Universita degli Studi di Milano, Via Comelico 39, 20135 Milano, Italia;

    DSI, Dipartimento di Scienze dell' Informazione, Universita degli Studi di Milano, Via Comelico 39, 20135 Milano, Italia;

  • 会议组织
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 TP274.3;
  • 关键词

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