AG Algorithmen und Komplexität
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A n^2 RNA Secondary Structure Prediction Algorithm

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Several state-of-the-art tools for predicting RNA secondary structures have worst-case time and space requirements of O(n3) and O(n2) for sequence length n, limiting their applicability for practical purposes. Accordingly, biologists are interested in getting results faster, where a moderate loss of accuracy would willingly be tolerated. For this reason, we propose a novel algorithm for structure prediction that reduces the time complexity by a linear factor to O(n2), while still being able to produce high quality results. Basically, our method relies on a probabilistic sampling approach based on an appropriate stochastic context-free grammar (SCFG):
using a well-known or a newly introduced sampling strategy it generates a random set of candidate structures (from the ensemble of all feasible foldings) according to a “noisy” distribution (obtained by heuristically approximating the inside-outside values) for a given sequence, such that finally a corresponding prediction can be efficiently derived. Sampling can easily be parallelized. Furthermore, it can be done in-place, i.e. only the best (most probable) candidate structure generated so far needs to be stored and finally communicated. Together, this allows to efficiently handle increased sample sizes necessary to achieve competitive prediction accuracy in connection with the noisy distribution.

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