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Predicting Golgi-resident protein types using pseudo amino acid compositions: Approaches with positional specific
1School of Computer Science and Technology, Tianjin University, Tianjin 300072, China.
Abstract:
Knowing the type of a Golgi-resident protein is an important step in understanding its molecular functions as well as its role in biological processes. In this paper, we developed a novel computational method to predict Golgi-resident protein types using positional specific physicochemical properties and analysis of variance based feature selection methods. Our method achieved 86.9% prediction accuracy in leave-one-out cross-validations with only 59 features. Our method has the potential to be applied in predicting a wide range of protein attributes.
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