Identification of biomarkers from mass spectrometry data using a "common" peak approach.
BACKGROUND
Proteomic data obtained from mass spectrometry have attracted great interest for the detection of early-stage cancer. However, as mass spectrometry data are high-dimensional, identification of biomarkers is a key problem.


RESULTS
This paper proposes the use of "common" peaks in data as biomarkers. Analysis is conducted as follows: data preprocessing, identification of biomarkers, and application of AdaBoost to construct a classification function. Informative "common" peaks are selected by AdaBoost. AsymBoost is also examined to balance false negatives and false positives. The effectiveness of the approach is demonstrated using an ovarian cancer dataset.


CONCLUSIONS
Continuous covariates and discrete covariates can be used in the present approach. The difference between the result for the continuous covariates and that for the discrete covariates was investigated in detail. In the example considered here, both covariates provide a good prediction, but it seems that they provide different kinds of information. We can obtain more information on the structure of the data by integrating both results.
Fushiki
Tadayoshi
T
Department of Mathematical Analysis and Statistical Inference, Institute of Statistical Mathematics, Tokyo, Japan. fushiki@ism.ac.jp
Fujisawa
Hironori
H
Eguchi
Shinto
S
D000465:Algorithms; D014408:Biomarkers, Tumor / Q000378:metabolism*; D019295:Computational Biology / Q000379:methods*; D005260:Female; D015972:Gene Expression Regulation, Neoplastic; D006801:Humans; D013058:Mass Spectrometry / Q000379:methods*; D015233:Models, Statistical; D009363:Neoplasm Proteins / Q000737:chemistry*; D010051:Ovarian Neoplasms / Q000235:genetics*; D040081:Protein Array Analysis / Q000379:methods*; D020543:Proteome; D040901:Proteomics / Q000379:methods*; D012984:Software
D014408:Biomarkers, Tumor; D009363:Neoplasm Proteins; D020543:Proteome