Learning stochastic motifs from genetic sequences:
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Bibliographische Detailangaben
Beteiligte Personen: Yamanishi, Kenji (VerfasserIn), Konagawa, Akihiko (VerfasserIn)
Format: Buch
Sprache:Englisch
Veröffentlicht: Tokyo, Japan 1991
Schriftenreihe:Shin-Sedai-Konpyūta-Gijutsu-Kaihatsu-Kikō <Tōkyō>: ICOT technical report 658
Schlagwörter:
Abstract:Abstract: "This paper presents a methodology for learning stochastic motifs from given genetic sequences. A stochastic motif here is a probabilistic mapping from a genetic sequence (which has been drawn from a finite alphabet) to a number of categories (cytochrome c, globin, trypsin, etc.). We propose a new representation of stochastic motifs, stochastic decision predicates (SDPs) and reduce our learning problem to that of learning SDPs. We employ Rissanen's Minimum Description Length (MDL) principle in selecting an optimal hypothesis and present a detailed method for calculating description lengths relative to SDPs. Experimental results show the validity of our learning strategy."
Umfang:5 S.
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Teilbibliothek Mathematik & Informatik, Berichte

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