Qualified Predictions for Proteomics Pattern Diagnostics with Confidence Machines

In this paper, we focus on the problem of prediction with confidence and describe the recently developed transductive confidence machines (TCM). TCM allows us to make predictions within predefined confidence levels, thus providing a controlled and calibrated classification environment. We apply the TCM to the problem of proteomics pattern diagnostics. We demonstrate that the TCM performs well, yielding accurate, well-calibrated and informative predictions in both online and offline learning settings.

[1]  Vladimir Vovk,et al.  On-line confidence machines are well-calibrated , 2002, The 43rd Annual IEEE Symposium on Foundations of Computer Science, 2002. Proceedings..

[2]  David Ward,et al.  Comparison of statistical methods for classification of ovarian cancer using mass spectrometry data , 2003, Bioinform..

[3]  R. Tibshirani,et al.  Diagnosis of multiple cancer types by shrunken centroids of gene expression , 2002, Proceedings of the National Academy of Sciences of the United States of America.

[4]  Alexander Gammerman,et al.  Prediction algorithms and confidence measures based on algorithmic randomness theory , 2002, Theor. Comput. Sci..

[5]  E. Petricoin,et al.  Use of proteomic patterns in serum to identify ovarian cancer , 2002, The Lancet.

[6]  Jeffrey S. Morris,et al.  Reproducibility of SELDI-TOF protein patterns in serum: comparing datasets from different experiments , 2004, Bioinform..

[7]  Vladimir Vapnik,et al.  Statistical learning theory , 1998 .

[8]  J. Glimm,et al.  Detection of cancer-specific markers amid massive mass spectral data , 2003, Proceedings of the National Academy of Sciences of the United States of America.