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Journal Article

Novel Search Method for the Discovery of Functional Relationships

MPS-Authors
http://pubman.mpdl.mpg.de/cone/persons/resource/persons45253

Ramirez,  Fidel
Computational Biology and Applied Algorithmics, MPI for Informatics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons44895

Lawyer,  Glenn
Computational Biology and Applied Algorithmics, MPI for Informatics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons43993

Albrecht,  Mario
Computational Biology and Applied Algorithmics, MPI for Informatics, Max Planck Society;

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btr631.pdf
(Any fulltext), 662KB

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Citation

Ramirez, F., Lawyer, G., & Albrecht, M. (2012). Novel Search Method for the Discovery of Functional Relationships. Bioinformatics, 28(2), 269-276. doi:10.1093/bioinformatics/btr631.


Cite as: http://hdl.handle.net/11858/00-001M-0000-0014-C810-0
Abstract
MOTIVATION: Numerous annotations are available that functionally characterize genes and proteins with regard to molecular process, cellular localization, tissue expression, protein domain composition, protein interaction, disease association and other properties. Searching this steadily growing amount of information can lead to the discovery of new biological relationships between genes and proteins. To facilitate the searches, methods are required that measure the annotation similarity of genes and proteins. However, most current similarity methods are focused only on annotations from the Gene Ontology (GO) and do not take other annotation sources into account. RESULTS: We introduce the new method BioSim that incorporates multiple sources of annotations to quantify the functional similarity of genes and proteins. We compared the performance of our method with four other well-known methods adapted to use multiple annotation sources. We evaluated the methods by searching for known functional relationships using annotations based only on GO or on our large data warehouse BioMyn. This warehouse integrates many diverse annotation sources of human genes and proteins. We observed that the search performance improved substantially for almost all methods when multiple annotation sources were included. In particular, our method outperformed the other methods in terms of recall and average precision.