Abstract
This paper proposes an approach to automating Gene Ontology (GO) annotation in the framework of hierarchical classification that uses known, already annotated functions of the orthologs of a given gene. The proposed approach exploits such known functions as constraints and dynamically builds classifiers based on the training data available under the constraints. In addition, two unsupervised approaches are applied to complement the classification framework. The validity and effectiveness of the proposed approach are empirically demonstrated.
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© 2009 Springer-Verlag Berlin Heidelberg
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Seki, K., Kino, Y., Uehara, K. (2009). Gene Functional Annotation with Dynamic Hierarchical Classification Guided by Orthologs. In: Gama, J., Costa, V.S., Jorge, A.M., Brazdil, P.B. (eds) Discovery Science. DS 2009. Lecture Notes in Computer Science(), vol 5808. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04747-3_35
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DOI: https://doi.org/10.1007/978-3-642-04747-3_35
Publisher Name: Springer, Berlin, Heidelberg
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