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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...
We propose an algorithm of searching for good discriminative gene sets (DGSs) in microarray cancer data, which we call active mining discriminative gene sets (AM-DGS). Tests in the leukemia data set and the prostate data set indicate that our method is able to achieve better accuracy with much smaller DGSs compared to 3 widely used methods, i.e., TS, FS, and SVM-RFE.
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