Functional Annotation of Genes Using Hierarchical Text Categorization

Abstract

This paper addresses the task of functional annotation of genes from biomedical literature. We view this task as a hierarchical text categorization problem with Gene Ontology as a class hierarchy. We present a novel global hierarchical learning approach that takes into account the semantics of a class hierarchy. This algorithm with AdaBoost as the underlying learning procedure significantly outperforms the corresponding “flat ” approach, i.e. the approach that does not consider any hierarchical information. In addition, we propose a novel hierarchical evaluation measure that gives credit to partially correct classification and discriminates errors by both distance and depth in a class hierarchy. 1.