Applications of semantic similarity measures

Abstract

There has been much interest in uncovering protein-protein interactions and
their underlying domain-domain interactions. Many experimental techniques
have been developed, for example yeast-two-hybrid screening and tandem
affinity purification. Since it is time consuming and expensive to perform
exhaustive experimental screens, in silico methods are used for predicting
interactions. However, all experimental and computational methods have
considerable false positive and false negative rates. Therefore, it is
necessary to validate experimentally determined and predicted interactions.

One possibility for the validation of interactions is the comparison of the
functions of the proteins or domains. Gene Ontology (GO) is widely accepted
as a standard vocabulary for functional terms, and is used for annotating
proteins and protein families with biological processes and their molecular
functions. This annotation can be used for a functional comparison of
interacting proteins or domains using semantic similarity measures.

Another application of semantic similarity measures is the prioritization
of disease genes. It is know that functionally similar proteins are often
involved in the same or similar diseases. Therefore, functional similarity
is used for predicting disease associations of proteins.

In the first part of my talk, I will introduce some semantic and functional
similarity measures that can be used for comparison of GO terms and
proteins or protein families. Then, I will show their application for
determining a confidence threshold for domain-domain interaction
predictions. Additionally, I will present FunSimMat
(http://www.funsimmat.de/), a comprehensive resource of functional
similarity values available on the web. In the last part, I will introduce
the problem of comparing diseases, and a first attempt to apply functional
similarity measures based on GO to this problem.