PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Semisupervised multiband segmentation for predictive proteomic analysis.
annalisa barla, bettina irler, silvano paoli, giuseppe jurman, stefano merler and cesare furlanello
In: Congress of European Proteomics Association, Feb-2007, Valencia, Spain.

Abstract

We present a predictive profiling system for functional proteomics that includes the semisupervised selection of different peak widths along the spectra regions. When dealing with mass spectrometry data, a crucial phase is represented by the peak identification step. In general,this goal is achieved by setting a parameter identifying peak width, as a function of the data and of the spectrometer. The classification performance can be significantly im- proved by varying thresholds on peak width on different bands of the analyzed spectra during the peak assignment step. In this paper we discuss a new multiresolution method, based on machine learning, by combining a proteomic preprocessing pipeline with the semisupervised procedures of BioDCV system, a complete validation setup based on the ERFE-SVM classifier. We present an evaluation of the new procedure on synthetic and real proteomic data.

EPrint Type:Conference or Workshop Item (Oral)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
ID Code:4113
Deposited By:annalisa barla
Deposited On:03 April 2008