Adaptive classifiers, topic drifts and GO annotations.
Gene annotations with Gene Ontology codes offer scientists important options in their study of genes and their functions. Automatic GO annotation methods have the potential to supplement the intensive manual annotation processes. Annotation approaches using MEDLINE documents are generally two-phased where the first is to annotate documents with GO codes and the second is to annotate gene products via the documents. In this paper we study document annotation with GO codes using a temporal perspective. Specifically, we build adaptive code-specific classifiers. We also study topic drift i.e., changes in the contextual characteristics of annotations over time. We show that topic drift is significant especially in the biological process GO hierarchy. This at least partially explains the particular challenges faced with codes of this hierarchy.
Srinivasan
Padmini
P
School of Library and Information Science, Department of Management Sciences, University of Iowa, USA. padmini-srinivasan@uiowa.edu
D019295:Computational Biology; D030541:Databases, Genetic; D005796:Genes; D016239:MEDLINE; D009323:Natural Language Processing; D009626:Terminology as Topic; D018875:Vocabulary, Controlled