Taxonomy-based soft similarity measures in bioinformatics

One of the most important objects in bioinformatics is a gene product (a protein or an RNA). Besides the gene sequence and expression values found following a microarray experiment, for many gene products, additional functional information comes from the set of gene ontology (GO) annotations and the set of journal abstracts related to the gene product. For these genes, it is reasonable to include similarity measures based on the terms found in the GO and/or the index term sets of the related documents (MeSH annotations). We propose a fuzzy measure-based similarity (FMS) for computing the similarity of two gene products annotated with terms from ontology. The advantage of FMS is that it takes into consideration the context of the whole set when computing the similarity. For the case when the two gene products are not annotated by common ontology terms, we propose a method that avoids a zero similarity result. In dealing with large groups of documents describing the objects under consideration, not only do we determine the similarity between the document pairs, but, by introducing the Choquet integral to the scenario, we can fuse this partial agreement function on pairs of documents into a single value relating the gene products. We present examples of FMS calculation for specific situations where two genes are described by a set of terms from the gene ontology, comparing our measures to others from the literature.