Neural Computing and Applications

, Volume 19, Issue 3, pp 471–475

Advanced machine learning techniques for microarray spot quality classification

Original Article

DOI: 10.1007/s00521-010-0342-3

Cite this article as:
Nanni, L., Lumini, A. & Brahnam, S. Neural Comput & Applic (2010) 19: 471. doi:10.1007/s00521-010-0342-3


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 

Copyright information

© Springer-Verlag London Limited 2010

Authors and Affiliations

  • Loris Nanni
    • 1
  • 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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