Computational Life Sciences II: Second International by Michael Hirsch, Allan Tucker, Stephen Swift, Nigel Martin,

By Michael Hirsch, Allan Tucker, Stephen Swift, Nigel Martin, Christine Orengo (auth.), Michael R. Berthold, Robert C. Glen, Ingrid Fischer (eds.)

This publication constitutes the refereed court cases of the second one overseas Symposium on Computational lifestyles Sciences, CompLife 2006, held in Cambridge, united kingdom, in September 2006.

The 25 revised complete papers offered have been conscientiously reviewed and chosen from fifty six preliminary submissions. The papers are equipped in topical sections on genomics, facts mining, molecular simulation, molecular informatics, structures biology, organic networks/metabolism, and computational neuroscience.

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Extra info for Computational Life Sciences II: Second International Symposium, CompLife 2006, Cambridge, UK, September 27-29, 2006. Proceedings

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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.

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