Incorporating secondary features into the general form of Chou's PseAAC for predicting protein structural class.
2012
Protein structure information is very useful for the confirmation of protein function. The protein structural class
can provide information for protein 3D structure analysis, causing the conformation of the protein overall folding type
plays a significant part in molecular biology. In this paper, we focus on the prediction of protein structural class which
was based on new feature representation. We extract features from the Chou-Fasman parameter, amino acid compositions,
amino acids hydrophobicity features, polarity information and pair-coupled amino acid composition. The prediction result
by the Support vector machine (SVM) classifier shows that our method is better than some others.
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