Gene Functional Annotation with Dynamic Hierarchical Classification Guided by Orthologs

摘要

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.