Applying Support Vector Machines for Gene ontology based gene function prediction

Background: The current progress in sequencing projects calls for rapid, reliable and accurate function assignments of gene products. A variety of methods has been designed to annotate sequences on a large scale. However, these methods can either only be applied for specific subsets, or their results are not formalised, or they do not provide precise confidence estimates for their predictions. Results: We have developed a large-scale annotation system that tackles all of these shortcomings. In our approach, annotation was provided through Gene Ontology terms by applying multiple Support Vector Machines (SVM) for the classification of correct and false predictions. The general performance of the system was benchmarked with a large dataset. An organism-wise crossvalidation was performed to define confidence estimates, resulting in an average precision of 80% for 74% of all test sequences. The validation results show that the prediction performance was organism-independent and could repr...

• Type: Journal
• Year: 2004
• Where: BMCBI
• Authors: Arunachalam Vinayagam, Rainer König, Jutta Moormann, Falk Schubert, Roland Eils, Karl-Heinz Glatting, Sándor Suhai
• Keywords: Annotation, BMCBI 2004, Confidence Estimates, Gene Ontology