Feature Selection for Classification of SELDI-TOF-MS Proteomic Profiles (original) (raw)

2005, Applied Bioinformatics

Background: Proteomic peptide profiling is an emerging technology harbouring great expectations to enable Abstract early detection, enhance diagnosis and more clearly define prognosis of many diseases. Although previous research work has illustrated the ability of proteomic data to discriminate between cases and controls, significantly less attention has been paid to the analysis of feature selection strategies that enable learning of such predictive models. Feature selection, in addition to classification, plays an important role in successful identification of proteomic biomarker panels. Methods: We present a new, efficient, multivariate feature selection strategy that extracts useful feature panels directly from the high-throughput spectra. The strategy takes advantage of the characteristics of surface-enhanced laser desorption/ionisation time-of-flight mass spectrometry (SELDI-TOF-MS) profiles and enhances widely used univariate feature selection strategies with a heuristic based on multivariate de-correlation filtering. We analyse and compare two versions of the method: one in which all feature pairs must adhere to a maximum allowed correlation (MAC) threshold, and another in which the feature panel is built greedily by deciding among best univariate features at different MAC levels. Results: The analysis and comparison of feature selection strategies was carried out experimentally on the pancreatic cancer dataset with 57 cancers and 59 controls from the

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