Download Algorithms in Bioinformatics: 12th International Workshop, by Matthias Bernt, Kun-Mao Chao, Jyun-Wei Kao (auth.), Ben PDF

By Matthias Bernt, Kun-Mao Chao, Jyun-Wei Kao (auth.), Ben Raphael, Jijun Tang (eds.)

This booklet constitutes the refereed complaints of the twelfth foreign Workshop on Algorithms in Bioinformatics, WABI 2012, held in Ljubljana, Slovenia, in September 2012. WABI 2012 is one in all six workshops which, besides the ecu Symposium on Algorithms (ESA), represent the ALGO annual assembly and makes a speciality of algorithmic advances in bioinformatics, computational biology, and platforms biology with a selected emphasis on discrete algorithms and machine-learning equipment that handle vital difficulties in molecular biology. The 35 complete papers awarded have been conscientiously reviewed and chosen from ninety two submissions. The papers contain algorithms for numerous organic difficulties together with phylogeny, DNA and RNA sequencing and research, protein constitution, and others.

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Extra resources for Algorithms in Bioinformatics: 12th International Workshop, WABI 2012, Ljubljana, Slovenia, September 10-12, 2012. Proceedings

Sample text

More than that, this approach can be quite slow; even for trees that consist of only a few thousand tips, existing software packages that use this method can take several minutes, or hours, to produce an output. Thus, it is interesting to ask the question if there can be algorithms that compute the expectation and the standard deviation of either the MPD and PD 32 C. Tsirogiannis, B. Sandel, and D. Cheliotis precisely, and at the same time efficiently, without having to tediously check each possible subset of tips of a certain size.

We denote this quantity by T C(e), thus: T C(e) = cost(u, v). {u,v}∈Δ(S) e∈p(u,v) It is easy to show that the latter quantity can be expressed as follows: T C(e) = (s − s(e)) wl · s(l) + s(e) l∈Off(e) = (s − s(e)) wl · s(l) + s(e) l∈Off(e) e∈E wl · s(l) l∈Ind(e) wl · (s − s(l)) (5) l∈Anc(e) wl · s(l) − + s(e) wl · (s − s(l)) + s(e) l∈Anc(e) wl · s(l) Off(e)∪Anc(e) For a node u that is a tip of T , we define the total path cost of u as the sum of the costs of all simple paths between u and any other tip of T .

The input trees are provided in Newick tree format [5] in a txt file. As a result, each separate execution of one of our algorithms with a specific input tree took linear time with respect to the input size, in order to read and parse the tree. As a point of reference for the efficiency of our implementation, we did the same experiments using the picante software package, which is an extension of the R software environment [10]. In the experiments that we conducted, we examined the sensitivity of running time to variation in tree size and sample size by generating random prunings of 40 C.

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