Titlebook: Algorithms in Bioinformatics; 11th International W Teresa M. Przytycka,Marie-France Sagot Conference proceedings 2011 Springer-Verlag GmbH

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Empirische Befunde: Forschungsprodukte(TF). Building an accurate bioinformatic model of TF-DNA binding is an essential step to differentiate true binding targets from spurious ones. Conventional approches of binding site prediction are based on the notion of consensus sequences. They are formalized by the so-called position-specific wei

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CYN

https://doi.org/10.1007/978-3-476-03187-7onjecture on the necessary and sufficient conditions of compatibility: Given a set . of .-states full characters, there exists a function .(.) such that . is compatible iff every set of .(.) characters of . is compatible. According to [7,9,8,25,11,23], .(2)?=?2, .(3)?=?3 and .(.)?≥?.. [23] conjectur

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https://doi.org/10.1007/978-3-642-91769-1ns and function annotations. Different from most, if not all, existing methods that only consider the partially labeled protein-protein interaction (PPI) network, the BG comprises three components, a PPI network, a function class graph induced from function annotations of such proteins, and a bipart

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https://doi.org/10.1007/978-3-642-91769-1, Boolean logic, or Petri nets. However, when it is important to understand quantitative dynamic features of a system, uncertainty about the networks often limits large-scale model development. Recent results, especially from monotone systems theory, suggest that structural network constraints can a

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Die Glykolyse und ihre Regulation,h the same is true in genome mapping of high-throughput short-read data. However, a highly sensitive search with multiple spaced patterns often requires the use of a great amount of index data. We propose a novel seed-set construction method for efficient and sensitive genome mapping of short reads

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https://doi.org/10.1007/978-3-642-47506-1likelihood (ML) or maximum a posterior (MAP) – are obtained and observation sequences are segmented based on the Viterbi path, even though the lack of accuracy and dependency on starting points of the local optimization are well known. We propose a method to speed-up Bayesian computations which addr

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https://doi.org/10.1007/978-3-658-03646-1minance and propose a novel combined approach based on Distance Alignment Search Tool (DAST), which contains an exact algorithm with bounds. Our experiments show that this method successfully finds the most similar proteins in a set without solving all instances.