Title
Advanced machine learning techniques for microarray spot quality classification
Abstract
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. © 2010 Springer-Verlag London Limited.
Department(s)
Information Technology and Cybersecurity
Document Type
Article
DOI
https://doi.org/10.1007/s00521-010-0342-3
Keywords
Feature selection, Microarray spot quality, Neural networks, Random subspace ensembles, Support vector machine
Publication Date
4-1-2010
Recommended Citation
Nanni, Loris, Alessandra Lumini, and Sheryl Brahnam. "Advanced machine learning techniques for microarray spot quality classification." Neural Computing and Applications 19, no. 3 (2010): 471-475.
Journal Title
Neural Computing and Applications
