Neural Computing and Applications

, Volume 19, Issue 3, pp 471–475 | Cite as

Advanced machine learning techniques for microarray spot quality classification

  • Loris NanniEmail author
  • Alessandra Lumini
  • Sheryl Brahnam
Original Article


It is well known that microarray printing, hybridization, and washing oftentimes create erroneous measurements, and these errors detrimentally impact machine microarray spot quality classification. Thus, it is crucial to identify and remove these errors if automation is to replace the still common practice of visually assessing spot quality, an extremely expensive and time-consuming procedure. A major problem in microarray spot quality classification methods proposed in the literature is the correlation among the features extracted from the spots. In this paper, we propose using a random subspace ensemble of neural networks and a feature selection algorithm to improve the performance of our microarray spot quality classification method. Our best method obtains an error under the receiver operating characteristic curve (EAUR) of 0.3 outperforming the stand-alone support vector machine EAUR of 1.7. The consistency of our proposed approach makes it a viable alternative to the labour-intensive manual method of spot quality assessment.


Random subspace ensembles Neural networks Support vector machine Feature selection Microarray spot quality 



The authors would like to thank S. Hautaniemi for sharing the data set.


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Copyright information

© Springer-Verlag London Limited 2010

Authors and Affiliations

  • Loris Nanni
    • 1
    Email author
  • Alessandra Lumini
    • 1
  • Sheryl Brahnam
    • 2
  1. 1.Department of Electronic, Informatics and Systems (DEIS)Università di BolognaCesenaItaly
  2. 2.Computer Information SystemsMissouri State UniversitySpringfieldUSA

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