By Renato Bruni (auth.), Renato Bruni (eds.)
Many difficulties bobbing up in organic, chemical and clinical learn, which may no longer be solved some time past because of their measurement and complexity, are these days tackled by way of computerized elaboration, therefore developing the rising box of Bioinformatics. even if, the good fortune of such techniques relies not just on brute computational energy of the pcs on which the answer methods run, but additionally, and infrequently severely, at the mathematical caliber of the types and of the algorithms underlying these answer methods. the current quantity deals an in depth evaluation of a few of the main attention-grabbing mathematical methods to series research and different series comparable difficulties. targeted emphasis is dedicated to difficulties in regards to the such a lot proper biopolymers (proteins and genetic sequences), however the exposition isn't restricted to them. the objective viewers contains researchers from many parts of Bioinformatics drawn to series research difficulties both from a theoretical and mathematical perspective, resembling mathematicians and machine scientists, or for extra applicative and production-oriented purposes, reminiscent of biologists and clinical researchers or practitioners operating for chemical or pharmaceutical businesses. The e-book should still additionally be of use to arithmetic scholars studying computational biology, or to biology scholars studying bioinformatics.
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Extra resources for Mathematical Approaches to Polymer Sequence Analysis and Related Problems
Example text
The prediction obtained is finally filtered (with the help of the reliability index) in order to fix some eventually unrealistic local predictions that neither the second level NN nor the consensus were able to detect (particularly, too short alpha-helix segments). The conservation weight provides a score for positions in the MSA with respect to their level of conservation: the more conserved is a position the higher is the conservation weight score. 5) CW i D PN r;sD1 wrs with 1 identrs ; 100 where N is the number of sequences in the multiple alignment, identrs is the percentage of sequence identity (over the entire length) of sequences r; s and simirs is the value of the similarity between sequences r; s at position i according to the Dayhoff similarity matrix [8].
E. Tarjan. A linear time algorithm for testing the truth of certain quantified boolean formulas. Information Processing Letters 8, 121–123 (1979) 2. V. Bafna and N. Edwards. On de novo interpretation of tandem mass spectra for peptide identification. In Annual Conference on Research in Computational Molecular Biology RECOMB03, 9–18 (2003) 22 R. Bruni 3. E. Boros, Y. L. Hammer. Polynomial time inference of all valid implications for Horn and related formulae. Annals of Mathematics and Artificial Intelligence 1, 21–32 (1990) 4.
References 1. B. F. E. Tarjan. A linear time algorithm for testing the truth of certain quantified boolean formulas. Information Processing Letters 8, 121–123 (1979) 2. V. Bafna and N. Edwards. On de novo interpretation of tandem mass spectra for peptide identification. In Annual Conference on Research in Computational Molecular Biology RECOMB03, 9–18 (2003) 22 R. Bruni 3. E. Boros, Y. L. Hammer. Polynomial time inference of all valid implications for Horn and related formulae. Annals of Mathematics and Artificial Intelligence 1, 21–32 (1990) 4.