By Michael Hirsch, Allan Tucker, Stephen Swift, Nigel Martin, Christine Orengo (auth.), Michael R. Berthold, Robert C. Glen, Ingrid Fischer (eds.)
This booklet constitutes the refereed court cases of the second one foreign Symposium on Computational existence Sciences, CompLife 2006, held in Cambridge, united kingdom, in September 2006.
The 25 revised complete papers awarded have been rigorously reviewed and chosen from fifty six preliminary submissions. The papers are geared up in topical sections on genomics, information mining, molecular simulation, molecular informatics, structures biology, organic networks/metabolism, and computational neuroscience.
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Additional info for Computational Life Sciences II: Second International Symposium, CompLife 2006, Cambridge, UK, September 27-29, 2006. Proceedings
Within the next section, we will examine a third technique that can equal the accuracies of the single model approach but uses less than half the number of attributes, resulting in a further speed improvement. e. an SVM), we can examine the weights associated with the attributes used. As described in the section materials and methods, input is provided to the SVM as a series of instances. Each instance describes the result of applying the given models to the sequence data returned by a sliding window of 150bps.
6] have presented one such algorithm for ﬁnding maximal approximate repeats of length L which runs in O(N + D3 z) by constructing a suﬃx tree where N is the length of the input string, z is the number of seeds and D is the maximum edit-distance expected in the resulting repeats. The expected number of seeds is E(z) = O(N 2 / | Σ | L/D+1 ). This is suitable for ﬁnding maximal approximate repeats with small edit-distances. For long repeats and large edit-distances (that is, L = c1 N and D = c2 L where c1 , c2 < 1 ), the expected running time of this algorithm is O(N 5 ).
Different values for these parameters result in different balances of sensitivity and specificity. This produces a window of varying length describing the promoter region. For the purposes of comparing TSS prediction position, we examined taking the start, middle and end of this window. Given the metrics we are using, we determined that taking either the middle or end produced the same results, but taking the start was markedly worse. Due to the fact that training times are quite long for some of the approaches we present herein (most notably the combined model with 839 attributes), we do not perform a ten-fold cross validation as is often done.
Computational Life Sciences II: Second International Symposium, CompLife 2006, Cambridge, UK, September 27-29, 2006. Proceedings by Michael Hirsch, Allan Tucker, Stephen Swift, Nigel Martin, Christine Orengo (auth.), Michael R. Berthold, Robert C. Glen, Ingrid Fischer (eds.)