GOPred: GO Molecular Function Prediction by Combined Classifiers

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2010-08-31

Author

Sarac, Oemer Sinan
Atalay, Mehmet Volkan
Atalay, Rengül

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Functional protein annotation is an important matter for in vivo and in silico biology. Several computational methods have been proposed that make use of a wide range of features such as motifs, domains, homology, structure and physicochemical properties. There is no single method that performs best in all functional classification problems because information obtained using any of these features depends on the function to be assigned to the protein. In this study, we portray a novel approach that combines different methods to better represent protein function. First, we formulated the function annotation problem as a classification problem defined on 300 different Gene Ontology (GO) terms from molecular function aspect. We presented a method to form positive and negative training examples while taking into account the directed acyclic graph (DAG) structure and evidence codes of GO. We applied three different methods and their combinations. Results show that combining different methods improves prediction accuracy in most cases. The proposed method, GOPred, is available as an online computational annotation tool (http://kinaz.fen.bilkent.edu.tr/gopred).

Subject Keywords

General Biochemistry, Genetics and Molecular Biology, General Agricultural and Biological Sciences, General Medicine

URI

https://hdl.handle.net/11511/34748

Journal

PLOS ONE

DOI

https://doi.org/10.1371/journal.pone.0012382

Collections

Department of Computer Engineering, Article

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Citation Formats

O. S. Sarac, M. V. Atalay, and R. Atalay, “GOPred: GO Molecular Function Prediction by Combined Classifiers,” PLOS ONE, pp. 0–0, 2010, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/34748.