By Claude Brezinski
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In other cases the top n weighted attributes are retained and the remainder are pruned. We specify which of these two approaches are being employed when we present the results. 2 Classifier and Post-processing We employ a support vector machine using a linear kernel, specifically the implementation present in WEKA [24-26] using the default settings, to classify instances as either promoter or non-promoter. We also post process the output of the support vector machine. The motivation for this is explained in more detail within the results section.
Adaptive filtering and prediction of the Southern Oscillation index. J. Geophys. Res. 97 (1992) 20449-20454 14. : Adaptive filtering and maximum entropy spectra with application to changes in atmospheric angular momentum. J. Geophys. Res. 96 (1991) 22659-22671 15. : Periodic gene expression program of the fission yeast cell cycle. Nature Genetics 36 (2004) 809-817 An Eﬃcient Algorithm for Finding Long Conserved Regions Between Genes Tak-Man Ma1 , Yuh-Dauh Lyuu2, , and Yen-Wu Ti2 2 1 Dept. edu Dept.
A combined approach, employing these techniques and other complementary (possibly sequence related) approaches may produce better results. In particular, this approach is currently not strand specific. That is, predictions are equally likely to be on either strand. By employing sequence based techniques, this could be overcome, allowing a strand specific prediction to be made. 30 P. M. Cameron-Jones, and A. Sale Acknowledgements This work was supported by an Australian Postgraduate Award. References 1.