Calculating PSSM probabilities with lazy dynamic programming

Malde K, Giegerich R (2006)
Jornal of Functional Programming 16(01): 75-81.

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Zeitschriftenaufsatz | Veröffentlicht | Englisch
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Abstract / Bemerkung
Position-specific scoring matrices are one way to represent approximate string patterns, which are commonly encountered in the field of bioinformatics. An important problem that arises with their application is calculating the statistical significance of matches. We review the currently most efficient algorithm for this task, and show how it can be implemented in Haskell, taking advantage of the built-in non-strictness of the language. The resulting program turns out to be an instance of dynamic programming, using lists rather the typical dynamic programming matrix.
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Zeitschriftentitel
Jornal of Functional Programming
Band
16
Zeitschriftennummer
01
Seite
75-81
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Malde K, Giegerich R. Calculating PSSM probabilities with lazy dynamic programming. Jornal of Functional Programming. 2006;16(01):75-81.
Malde, K., & Giegerich, R. (2006). Calculating PSSM probabilities with lazy dynamic programming. Jornal of Functional Programming, 16(01), 75-81. doi:10.1017/S0956796805005708
Malde, K., and Giegerich, R. (2006). Calculating PSSM probabilities with lazy dynamic programming. Jornal of Functional Programming 16, 75-81.
Malde, K., & Giegerich, R., 2006. Calculating PSSM probabilities with lazy dynamic programming. Jornal of Functional Programming, 16(01), p 75-81.
K. Malde and R. Giegerich, “Calculating PSSM probabilities with lazy dynamic programming”, Jornal of Functional Programming, vol. 16, 2006, pp. 75-81.
Malde, K., Giegerich, R.: Calculating PSSM probabilities with lazy dynamic programming. Jornal of Functional Programming. 16, 75-81 (2006).
Malde, Ketil, and Giegerich, Robert. “Calculating PSSM probabilities with lazy dynamic programming”. Jornal of Functional Programming 16.01 (2006): 75-81.