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Using mRNA Secondary Structure Predictions Improves Recognition of Known Yeast Functional uORFs

. Selpi ; C. H. Bryant ; Graham J.L. Kemp (Institutionen för data- och informationsteknik, Datavetenskap, Bioinformatik (Chalmers))
Wehenkel, L., d'Alché-Buc, F., Moreau, Y. and Geurts, P. (eds.) MLSB08, The Second International Workshop on Machine Learning in Systems Biology, Brussels, 13-14 September 2008. p. 85-93. (2008)
[Konferensbidrag, refereegranskat]

We are interested in using inductive logic programming (ILP) to generate rules for recognising functional upstream open reading frames (uORFs) in the yeast Saccharomyces cerevisiae. This paper empirically investigates whether providing an ILP system with predicted mRNA secondary structure can increase the performance of the resulting rules. Two sets of experiments, with and without mRNA secondary structure predictions as part of the background knowledge, were run. For each set, stratified 10-fold cross-validation experiments were run 100 times, each time randomly permuting the order of the positive training examples, and the performance of the resulting hypotheses were measured. Our results demonstrate that the performance of an ILP system in recognising known functional uORFs in the yeast S. cerevisiae significantly increases when mRNA secondary structure predictions are added to the background knowledge and suggest that mRNA secondary structure can affect the ability of uORFs to regulate gene expression.



Denna post skapades 2009-01-11. Senast ändrad 2010-01-08.
CPL Pubid: 84980

 

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