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Aiming at the prediction of protein solubility, four feature reduction methods are discussed in this paper, including Correlation coefficient method, Filter method, Relief method and Genetic method. With the Top 100 features discovered by genetic method, the best classifier achieves the accuracy of 86% and MCC of 0.7236 in Jackknife test. Moreover, further discussions about feature reduction and classifier...
Predicting protein folding rate is useful for understanding protein folding process and guiding protein design. Most previous methods of predicting folding rate require the tertiary structure of a protein as an input. And most methods do not distinguish the different kinetic natures (two-state folding and multi-state folding) of the proteins. Here we developed a method, SeqRate, to predict both protein...
The support vector machine (SVM) method based on n-peptide composition (Yu et al, Proteins: Struct. Funct. Genet. 2003:50:531-536) is used to predict the subcellular localizations of proteins. For an unbiased assessment of the results, we apply our approach to two independent data sets: one set consisting of two parts (Reinhardt and Hubbard, Nucleic Acids Res. 1998; 26:2230-2236): the prokaryotic...
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