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Feature selection plays an important role in cancer classification, for gene expression data usually have a large number of dimensions and relatively a small number of samples. In this paper, we use the support vector machine (SVM) for cancer classification. We propose a mixed two-step feature selection method. The first step uses a modified t-test method to select discriminatory features. The second...
In this paper, we develop a method for predicting signal peptides and their cleavage sites. Unlike other published work, we divide proteins into two segments and calculate the amino acid compositions on both segments. After that, we hybridize the pseudo amino acid compositions (PseAAs) to the feature vectors. Using support vector machines (SVMs) to train the datasets, we get better results than those...
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