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Understanding protein structures is vital to determining the function of a protein and its interaction with DNA, RNA and enzyme. The information about its conformation can provide essential information for drug design and protein engineering. While there are over a million known protein sequences, only a limited number of protein structures are experimentally determined. Hence, prediction of protein...
The machine learning techniques, SVM, decision tree, and decision rule, are used to predict the susceptibility to the liver disease, chronic hepatitis from single nucleotide polymorphism(SNP) data. Also, they are used to identify a set of SNPs relevant to the disease. In addition, we apply backtracking technique to couple of feature selection algorithms, forward selection and backward elimination,...
Decision tree is one common method used in data mining to extract predicted information. Based on Statistical Learning Theory (SLT), support vector machine(SVM) is a new kind of machine learning method that is used for classification and regression, it realizes the trade-off between empirical risk minimization(ERM) and generalization capability. SVM and decision tree have combined into one multi-class...
The induction of classification of decision tree is an important algorithm for data mining now. The support vector machine technology and the decision tree have combined into one multi-class classifier so as to solve multi-class classification problems. In this paper, SVM is extended to non-linear SVM by using kernel functions and a new method of NSVM decision tree is proposed based on traditional...
Our investigation aims at extending kernel methods to interval data mining and using graphical methods to explain the obtained results. Interval data type can be a good way to aggregate large datasets into smaller ones or to represent data with uncertainty. No algorithmic changes are required from the usual case of continuous data other than the modification of the radial basis kernel function evaluation...
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