A Theory for Multiresolution Signal Decomposition: The Wavelet Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Programs for Digital Signal Processing
Programs for Digital Signal Processing
IEEE Intelligent Systems
Find Key m/z Values in Predication of Mass Spectrometry Cancer Data
ICIC '08 Proceedings of the 4th international conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications - with Aspects of Theoretical and Methodological Issues
Dimensionality reduction and main component extraction of mass spectrometry cancer data
Knowledge-Based Systems
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Mass spectrometry is being used to generate protein profiles from human serum, and proteomic data obtained from mass spectrometry have attracted great interest for the detection of early-stage cancer. However, high dimensional mass spectrometry data cause considerable challenges. In this paper a set of wavelet detail coefficients at different levels is used to characterize the localized changes of mass spectrometry data and reduce dimensionality of mass spectra. The experiments are performed on high resolution ovarian dataset. A highly competitive accuracy compared to the best performance of other kinds of classification models is achieved.