Improving computational predictions of cis-regulatory binding sites.
The location of cis-regulatory binding sites determine the connectivity of genetic regulatory networks and therefore constitute a natural focal point for research into the many biological systems controlled by such regulatory networks. Accurate computational prediction of these binding sites would facilitate research into a multitude of key areas, including embryonic development, evolution, pharmacogenemics, cancer and many other transcriptional diseases, and is likely to be an important precursor for the reverse engineering of genome wide, genetic regulatory networks. Many algorithmic strategies have been developed for the computational prediction of cis-regulatory binding sites but currently all approaches are prone to high rates of false positive predictions, and many are highly dependent on additional information, limiting their usefulness as research tools. In this paper we present an approach for improving the accuracy of a selection of established prediction algorithms. Firstly, it is shown that species specific optimization of algorithmic parameters can, in some cases, significantly improve the accuracy of algorithmic predictions. Secondly, it is demonstrated that the use of non-linear classification algorithms to integrate predictions from multiple sources can result in more accurate predictions. Finally, it is shown that further improvements in prediction accuracy can be gained with the use of biologically inspired post-processing of predictions.
Robinson
Mark
M
Science and Technology Research Institute, University of Hertfordshire, College Lane Hatfield, Hertfordshire AL10 9AB, UK. m.robinson@herts.ac.uk
Sun
Yi
Y
Boekhorst
Rene Te
RT
Kaye
Paul
P
Adams
Rod
R
Davey
Neil
N
Rust
Alistair G
AG
D000465:Algorithms; D001185:Artificial Intelligence; D001665:Binding Sites / Q000235:genetics; D019295:Computational Biology; D004247:DNA / Q000235:genetics* / Q000378:metabolism*; D030562:Databases, Protein; D005786:Gene Expression Regulation; D023281:Genomics / Q000706:statistics & numerical data; D040901:Proteomics / Q000706:statistics & numerical data
D004247:DNA