This Article 
   
 Share 
   
 Bibliographic References 
   
 Add to: 
 
Digg
Furl
Spurl
Blink
Simpy
Google
Del.icio.us
Y!MyWeb
 
 Search 
   
Biomarker Signature Discovery From Mass Spectrometry Data
PrePrint
ISSN: 1545-5963
Ao Kong, Ao Kong is with the Dept of Mathematics, University of Houston, TX, USA.
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 MALDITOF dataset, and two well-known ovarian cancer SELDI-TOF datasets. In all 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.
Citation:
Ao Kong, Chinmaya Gupta, Mauro Ferrari, Marco Agostini, Chiara Bedin, Ali Bouamrani, Ennio Tasciotti, Robert Azencott, "Biomarker Signature Discovery From Mass Spectrometry Data," IEEE/ACM Transactions on Computational Biology and Bioinformatics, 21 April 2014. IEEE computer Society Digital Library. IEEE Computer Society, <http://doi.ieeecomputersociety.org/10.1109/TCBB.2014.2318718>
Usage of this product signifies your acceptance of the Terms of Use.