By Srinivas Aluru (auth.), Guohui Lin (eds.)

The Annual overseas Computing and Combinatorics convention is an annual discussion board for exploring learn, improvement, and novel purposes of computing and combinatorics. It brings jointly researchers, execs and business practitioners to have interaction and alternate wisdom, rules and development. Thetopics covermost features oftheoreticalcomputer scienceand combinatorics with regards to computing. The thirteenth Annual overseas Computing and Com- natorics convention (COCOON 2007) was once held in Ban?, Alberta in the course of July 16–19, 2007. This used to be the ?rst time that COCOON was once held in Canada. We obtained a hundred sixty five submissions, between which eleven have been withdrawn for varied purposes. the remainder 154 submissions less than complete attention got here from 33 international locations and areas: Australia, Brazil, Canada, China, the Czech Republic, Denmark, Finland, France, Germany, Greece, Hong Kong, India, Iran, eire, Israel, Italy, Japan, the Netherlands, Norway, Pakistan, Poland, Romania, R- sia, Slovakia, South Korea, Spain, Sweden, Switzerland, Taiwan, Turkey, the united kingdom, the united states, and the USA minor outlying islands. Afterasixweekperiodofcarefulreviewinganddiscussions,theprogramc- mittee authorised fifty one submissions for oral presentation on the convention. in line with the a?liations, 1. 08 of the approved papers have been from Australia, 7. sixty seven from Canada, three. 08 from China, 1 from the Czech Republic, 2 from Denmark, 1 from France, five. forty two from Germany, zero. 08 from Greece, 2. 18 from Hong Kong, zero. 33 from India, zero. 17 from eire, 1. eighty three from Israel, 1. 5fromItaly,2. nine from Japan, zero. 17 from the Netherlands, 2. sixty seven from Norway, 0.

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Extra resources for Computing and Combinatorics: 13th Annual International Conference, COCOON 2007, Banff, Canada, July 16-19, 2007. Proceedings

Example text

We have shown that given sub-motifs with small degeneracy values, a hashing method built on the preprocessing of the target database can significantly improve search times. The idea is to select in the descriptor anchors which yield the least computation. That being the case, it’s not given that any descriptor contains enough consecutive conservations to permit sublinear filtering. By using distance constraints we can significantly reduce the number of needed consecutive conserved positions by introducing gaps between pairs of anchors.

Not the derived states). But since the forest produced by the history bound is a valid phylogenetic forest, its number of trees in that forest cannot be smaller than the forest bound. We now relate the forest bound to the optimal haplotype bound. Theorem 1. The forest bound is higher than the optimal haplotype bound. Proof. By Lemma 3 we know that the forest bound applied to any subset of site is higher than the haplotype bound applied to the same subset of sites. In particular, if S ∗ is the subset of sites of M (called optimal subset ) that gives the optimal haplotype bound, then the forest bound applied to S ∗ is higher.

Given a sequence u of size k on ΣDNA , we denote by Oc(u) the list of positions of all occurrences of u in the genomic sequence G, eventually within a threshold of error e. The occurrences list of a stem-loop described by u is: S(u,u) = {(p, q) | p ∈ Oc(u), q ∈ Oc(u), good(p, q)} The predicate good(p, q) checks the distance (dmin ≤ q − p ≤ dmax ) and the error constraints. The algorithm proceeds by successive extensions and filtering steps, starting from sets Oc(α) for each α ∈ ΣDNA . Each set Oc(ui,j ) could be constructed by extending Oc(ui+1,j ) and Oc(ui,j−1 ).

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