Profiling of Mass Spectrometry Data for Ovarian Cancer Detection Using Negative Correlation Learning

Authors:
Shan He;Huanhuan Chen;Xiaoli Li;Xin Yao
Affiliations:
The Centre of Excellence for Research in Computational Intelligence and Application (Cercia) School of Computer Science, University of Birmingham, Birmingham, United Kingdom B15 2TT;The Centre of Excellence for Research in Computational Intelligence and Application (Cercia) School of Computer Science, University of Birmingham, Birmingham, United Kingdom B15 2TT;The Centre of Excellence for Research in Computational Intelligence and Application (Cercia) School of Computer Science, University of Birmingham, Birmingham, United Kingdom B15 2TT;The Centre of Excellence for Research in Computational Intelligence and Application (Cercia) School of Computer Science, University of Birmingham, Birmingham, United Kingdom B15 2TT
Venue:
ICANN '09 Proceedings of the 19th International Conference on Artificial Neural Networks: Part II
Year:
2009

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Abstract

This paper proposes a novel Mass Spectrometry data profiling method for ovarian cancer detection based on negative correlation learning (NCL). A modified Smoothed Nonlinear Energy Operator (SNEO) and correlation-based peak selection were applied to detected informative peaks for NCL to build a prediction model. In order to evaluate the performance of this novel method without bias, we employed randomization techniques by dividing the data set into testing set and training set to test the whole procedure for many times over. The classification performance of the proposed approach compared favorably with six machine learning algorithms.