title = {Classification of SELDI-ToF mass spectra of ovarian cancer serum samples using a proteomic pattern recognizer},
  booktitle = {Bioengineering Conference, 2003 IEEE 29th Annual, Proceedings of},
  year = {2003},
  month = mar,
  pages = {130-131},
  keywords = {analytical challenges, biology computing, cancer, classification accuracy, complex data, computational challenges, computational speed, Data mining, discriminatory subsets, fast pattern recognition system, feature extraction, Filtering algorithms, Filters, genetic algorithm, genetic algorithms, genetics, high dimensionality, high discriminatory power, high-throughput mass spectrometry technologies, mass spectroscopic chemical analysis, Mass spectroscopy, medical diagnostic computing, molecular weight, neural nets, noise, ovarian cancer serum samples, pattern classification, pattern recognition, photon stimulated desorption, proteins, proteomic pattern recognizer, Proteomics, SELDI-ToF mass spectra classification, spectrum classification, spectrum subsets, Surface emitting lasers, surface enhanced laser desorption/ionization time-of-flight mass spectrometry, time of flight mass spectroscopy},
  doi = {10.1109/NEBC.2003.1216026},
  author = {Loo, Lit-Hsin and Quinn, J. and Cordingley, H. and Roberts, S. and Hrebien, Leonid and Kam, Moshe}