Genomic processing for cancer classification and prediction - Abroad review of the recent advances in model-based genomoric and proteomic signal processing for cancer detection

This article discusses signal processing and modelling of genomic and proteomic data from two cutting edge technologies, namely microarray technology and mass spectrometry (MS) technologies, as they are clearly among the leading frontiers that can reshape cancer study. The paper is organised as follows: first, a review of the few major design methodologies for cancer classification and prediction using genomic pr proteomic data. We then present an ensemble dependence model (EDM)-based framework and discuss the concept of dependence network. The EDM network is applied to both microarray gene expression and MS data sets in cancer study. We also present the performance-based idea and dependence network-based idea for biomarker identification. Our goal is to provide a broad review of the recent advances on model-based genomic and proteomic signal processing for cancer detection and prediction