Prediction of Drosophila melanogaster gene function using Support Vector Machines

BioData Mining

Nicholas Mitsakakis³  Zak Razak²  Michael Escobar⁴  J Timothy Westwood¹ 

[1] Department of Cell and Systems Biology, University of Toronto at Mississauga, Mississauga, Canada

[2] Canadian Drosophila Microarray Centre, University of Toronto at Mississauga, Mississauga, Canada

[3] Toronto Health Economics and Technology Assessment (THETA) Collaborative, University of Toronto, Toronto, Canada

[4] Dalla Lana School of Public Health, University of Toronto, Toronto, Canada

关键词: Gene function prediction;    Gene expression data;    Drosophila melanogaster;    Support Vector Machines;    Gene ontology;   

Others  :  797198
DOI  :  10.1186/1756-0381-6-8

 received in 2012-06-12, accepted in 2013-02-11,  发布年份 2013

【 摘 要 】

Background

While the genomes of hundreds of organisms have been sequenced and good approaches exist for finding protein encoding genes, an important remaining challenge is predicting the functions of the large fraction of genes for which there is no annotation. Large gene expression datasets from microarray experiments already exist and many of these can be used to help assign potential functions to these genes. We have applied Support Vector Machines (SVM), a sigmoid fitting function and a stratified cross‐validation approach to analyze a large microarray experiment dataset from Drosophila melanogaster in order to predict possible functions for previously un‐annotated genes. A total of approximately 5043 different genes, or about one‐third of the predicted genes in the D. melanogaster genome, are represented in the dataset and 1854 (or 37%) of these genes are un‐annotated.

Results

39 Gene Ontology Biological Process (GO‐BP) categories were found with precision value equal or larger than 0.75, when recall was fixed at the 0.4 level. For two of those categories, we have provided additional support for assigning given genes to the category by showing that the majority of transcripts for the genes belonging in a given category have a similar localization pattern during embryogenesis. Additionally, by assessing the predictions using a confidence score, we have been able to provide a putative GO‐BP term for 1422 previously un‐annotated genes or about 77% of the un‐annotated genes represented on the microarray and about 19% of all of the un‐annotated genes in the D. melanogaster genome.

Conclusions

Our study successfully employs a number of SVM classifiers, accompanied by detailed calibration and validation techniques, to generate a number of predictions for new annotations for D. melanogaster genes. The applied probabilistic analysis to SVM output improves the interpretability of the prediction results and the objectivity of the validation procedure.

【 授权许可】

   
2013 Mitsakakis et al.; licensee BioMed Central Ltd.

【 图 表 】

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