Advanced machine learning techniques for microarray spot quality classification
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.
KeywordsRandom subspace ensembles Neural networks Support vector machine Feature selection Microarray spot quality
- 7.Model F, König T, Piepenbrock C, Adorján P (2002) Statistical process control for large scale microarray experiments. Bioinformatics 1:1–9Google Scholar
- 9.RuosaariS, Hollmén J (2002) Image analysis for detecting faulty spots from microarray images. In: LangeS, Satoh K, Smith CH (eds) Proceedings of the 5th international conference on discovery science (DS2002). Springer, Berlin, pp 259–266Google Scholar
- 15.Cristianini N, Shawe-Taylor J (2000) An introduction to support vector machines and other kernel-based learning methods. Cambridge University Press, CambridgeGoogle Scholar
- 18.Brahnam S, Nanni L, Randall S (2007) Introduction to neonatal facial pain detection using common and advanced face classification techniques. In: Advanced computational intelligence paradigms in healthcare, vol 48, Springer Berlin, pp 225–253Google Scholar
- 19.Huang L, Dai Y (2005) A support vector machine approach for prediction of T cell epitopes. In: Proceedings of the third Asia-Pacific bioinformatics conference (APBC2005), Singapore, Jan 17–21, pp 312–328Google Scholar