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Approximation and Exact Algorithms for RNA Secondary Structure Prediction and Recognition of Stochastic Context-Free Languages

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Algorithms and Computation (ISAAC 1998)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1533))

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Abstract

For a basic version (i.e., maximizing the number of basepairs) of the RNA secondary structure prediction problem and the construction of a parse tree for a stochastic context-free language, O(n3) time algorithms were known. For both problems, this paper shows slightly improved O(n3(log log n)1/2/(log n)1/2) time exact algorithms. Moreover, this paper shows an O(n2.776) time approximation algorithm for the former problem and an O(n2.976 log n) time approximation algorithm for the latter problem, each of which has a guaranteed approximation ratio 1 -/ge for any fixed constant /ge > 0, where the absolute value of the logarithm of the probability is considered as an objective value in the latter problem. Several related results are shown too.

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Author information

Authors and Affiliations

  1. Human Genome Center, Institute of Medical Science, University of Tokyo, 4-6-1 Shirokanedai, Minato-ku, Tokyo, 108-8639, Japan

    Tatsuya Akutsu

Authors
  1. Tatsuya Akutsu
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Editors and Affiliations

  1. Department of Computer Science, Korea Advanced Institute of Science and Technology, 373-1, Kusong-dong, Yusong-gu, Taejon, 305-701, Korea

    Kyung-Yong Chwa

  2. Department of Computer Science, University of California, Santa Barbara, CA, 93106, USA

    Oscar H. Ibarra

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© 1998 Springer-Verlag Berlin Heidelberg

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Akutsu, T. (1998). Approximation and Exact Algorithms for RNA Secondary Structure Prediction and Recognition of Stochastic Context-Free Languages. In: Chwa, KY., Ibarra, O.H. (eds) Algorithms and Computation. ISAAC 1998. Lecture Notes in Computer Science, vol 1533. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-49381-6_36

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  • DOI: https://doi.org/10.1007/3-540-49381-6_36

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-65385-1

  • Online ISBN: 978-3-540-49381-5

  • eBook Packages: Springer Book Archive

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