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The combined use of gene expression profiles and protein-protein interaction (PPI) networks has recently shed light on breast cancer research by selecting a small number of subnetworks as disease markers and then using them for the classification of metastasis. Based on previously identified subnetwork markers, we compare three ensemble learning approaches (AdaBoost, LogitBoost and random forest)...
Summary form only given. The current molecular biology and systems biology is featured by the rapid accumulation of high-throughput genomics and proteomics data like microarray and mass spectrometry (MS) data. Through our study on microarray and MS data, we have observed that the cancer classification and gene/biomarker selection task has many unique characteristics that distinguish itself from other...
Many methods for classification and gene selection with microarray data have been developed. These methods usually give a ranking of genes. Evaluating the statistical significance of the gene ranking is important for understanding the results and for further biological investigations, but this question has not been well addressed for machine learning methods in existing works. Here, we address this...
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