Biomarker signature discovery from mass spectrometry data

Ao Kong, Chinmaya Gupta, Mauro Ferrari, Marco Agostini, Chiara Bedin, Ali Bouamrani, Ennio Tasciotti, Robert Azencott

Research output: Contribution to journalArticlepeer-review

10 Scopus citations


Mass spectrometry based high throughput proteomics are used for protein analysis and clinical diagnosis. Many machine learning methods have been used to construct classifiers based on mass spectrometry data, for discrimination between cancer stages. However, the classifiers generated by machine learning such as SVM techniques typically lack biological interpretability. We present an innovative technique for automated discovery of signatures optimized to characterize various cancer stages. We validate our signature discovery algorithm on one new colorectal cancer MALDI-TOF data set, and two well-known ovarian cancer SELDI-TOF data sets. In all of these cases, our signature based classifiers performed either better or at least as well as four benchmark machine learning algorithms including SVM and KNN. Moreover, our optimized signatures automatically select smaller sets of key biomarkers than the black-boxes generated by machine learning, and are much easier to interpret.

Original languageEnglish (US)
Article number6802429
Pages (from-to)766-772
Number of pages7
JournalIEEE/ACM Transactions on Computational Biology and Bioinformatics
Issue number4
StatePublished - 2014


  • Automatic signature discovery
  • Biomarker selection
  • Colorectal cancer
  • MALDI/SELDI data
  • Ovarian cancer

ASJC Scopus subject areas

  • Biotechnology
  • Genetics
  • Applied Mathematics


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