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This thesis is concerned with the design of efficient algorithms for listing combinatorial structures. The research described here gives some answers to the .
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New Releases. Description First published in , this thesis is concerned with the design of efficient algorithms for listing combinatorial structures. The research described here gives some answers to the following questions: which families of combinatorial structures have fast computer algorithms for listing their members? What general methods are useful for listing combinatorial structures?
Efficient Algorithms for Listing Combinatorial Structures
How can these be applied to those families which are of interest to theoretical computer scientists and combinatorialists? Amongst those families considered are unlabelled graphs, first order one properties, Hamiltonian graphs, graphs with cliques of specified order, and k-colourable graphs.
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Some related work is also included, which compares the listing problem with the difficulty of solving the existence problem, the construction problem, the random sampling problem, and the counting problem. In particular, the difficulty of evaluating Polya's cycle polynomial is demonstrated. Other books in this series. Qualified Types Mark P.
Add to basket. In order to partially answer this question, we introduce the formal definition of the computational problem of reconstructing the gene structure from RNA-Seq data when the solution is represented by a splicing graph and we give some necessary conditions under which the reconstructed splicing graph represents the real unknown gene structure. Finally we describe an efficient algorithm that, under some conditions, is able to exactly solve our problem but that is also able to achieve good accuracy on real genes violating such conditions. In such a way, they negatively regulate gene expression; however, the exact mechanism remains still unclear.
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Computational prediction of miRNA targets is not an easy task. In the last few years, more than 10 target prediction programs have been developed. They typically analyze sequences by a sliding window and, in order to reduce false positive predictions, they test each site of the gene with some algorithms in cascade. It splits the target site prediction task into three distinct steps carried out in sequence: a Homology evaluation; b Free energy computation; c Evolutionary conservation computation.
The parameters used in miRanda have been manually optimized according to biological knowledge and software tools available in with some updates. We present an approach to improve the computational estimation of target sites, maximizing the adherence of results to biological evidence. The proposed method provides some improvements on the original miRanda program. In particular, we employ the new RNAcofold routine for free energy computation and we improve the match between the set of estimated target genes and the set of biologically validated target genes nowadays known by using a genetic algorithm.
Analysis of algorithms - Wikipedia
Our method is more selective than miRanda. The validated targets with the proposed method have consistently better free energy than the others in the list; this is not true for miRanda. The increased selectivity of the method may also be used to guide experimental validation in a more focused direction. In the classical exact string matching problem, we are given two strings s the text and t the pattern , and we want to find all occurrences of t as substrings of s.