@inproceedings{4bef977a2257492bb121501608d5891e,
title = "Profiling of mass spectrometry data for ovarian cancer detection using negative correlation learning",
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. {\textcopyright} 2009 Springer Berlin Heidelberg.",
keywords = "Bioinformatics, Data mining, Negative correlation learning, Proteomics",
author = "Shan HE and Huanhuan CHEN and Xiaoli LI and Xin YAO",
year = "2009",
doi = "10.1007/978-3-642-04277-5_19",
language = "English",
isbn = "9783642042768",
series = "Lecture Notes in Computer Science",
publisher = "Springer Berlin Heidelberg",
pages = "185--194",
editor = "Cesare APLIPPI and Marios POLYCARPOU and Christos PANAYIOTOU and Georgios ELLINAS",
booktitle = "Artificial Neural Networks : ICANN 2009 19th International Conference, Limassol, Cyprus, September 14-17, 2009, Proceedings, Part II",
note = "19th International Conference on Artificial Neural Networks, ICANN 2009 ; Conference date: 14-09-2009 Through 17-09-2009",
}
