title: Information content-based Gene Ontology functional similarity measures: which one to use for a given biological data type?
abstract: The current increase in Gene Ontology (GO) annotations of proteins in the existing genome databases and their use in different analyses have fostered the improvement of several biomedical and biological applications. To integrate this functional data into different analyses, several protein functional similarity measures based on GO term information content (IC) have been proposed and evaluated, especially in the context of annotation-based measures. In the case of topology-based measures, each approach was set with a specific functional similarity measure depending on its conception and applications for which it was designed. However, it is not clear whether a specific functional similarity measure associated with a given approach is the most appropriate, given a biological data set or an application, i.e., achieving the best performance compared to other functional similarity measures for the biological application under consideration. We show that, in general, a specific functional similarity measure often used with a given term IC or term semantic similarity approach is not always the best for different biological data and applications. We have conducted a performance evaluation of a number of different functional similarity measures using different types of biological data in order to infer the best functional similarity measure for each different term IC and semantic similarity approach. The comparisons of different protein functional similarity measures should help researchers choose the most appropriate measure for the biological application under consideration.
authors.affiliation: Computational Biology Group, Department of clinical Laboratory Sciences, IDM, University of Cape Town Faculty of Health Sciences, Cape Town, South Africa; African Institute for Mathematical Sciences (AIMS), Cape Town, South Africa, and Cape Coast, Ghana.
authors.forename: Gaston K
authors.initials: GK
authors.lastname: Mazandu
authors.affiliation: Computational Biology Group, Department of clinical Laboratory Sciences, IDM, University of Cape Town Faculty of Health Sciences, Cape Town, South Africa.
authors.forename: Nicola J
authors.initials: NJ
authors.lastname: Mulder
mesh_terms: D019540:Area Under Curve; D016000:Cluster Analysis; D019295:Computational Biology; D016208:Databases, Factual; D030562:Databases, Protein; D006801:Humans; D058977:Molecular Sequence Annotation; D060066:Protein Interaction Maps; D011506:Proteins / Q000737:chemistry / Q000235:genetics / Q000378:metabolism; D012372:ROC Curve
chemical_list: D011506:Proteins