Article ID Journal Published Year Pages File Type
4495928 Journal of Theoretical Biology 2016 8 Pages PDF
Abstract

•A novel protein sequence representation incorporating evolutionary information.•A better form of pseudo-amino acid compositions.•A minimal set of features to predict Golgi-resident protein types.

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.

Related Topics
Life Sciences Agricultural and Biological Sciences Agricultural and Biological Sciences (General)
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