IEEE Transactions on Pattern Analysis and Machine Intelligence
Bioinformatics: the machine learning approach
Bioinformatics: the machine learning approach
Learning Context-Free Grammars from Partially Structured Examples
ICGI '00 Proceedings of the 5th International Colloquium on Grammatical Inference: Algorithms and Applications
A hybrid language model based on a combination of N-grams and stochastic context-free grammars
ACM Transactions on Asian Language Information Processing (TALIP)
Extracting grammar from programs: evolutionary approach
ACM SIGPLAN Notices
Grammatical Inference in Bioinformatics
IEEE Transactions on Pattern Analysis and Machine Intelligence
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In the paper, we describe an application of stochastic context-free grammars (SCFG) to modelling of the formal RNA string language. The simplification of the stochastic context-free grammar and it's conversion to Chomsky normal form was used. We present the modification of Cocke-Kasami-Younger algorithm that is used for probabilistic estimations of stochastic grammars for RNA sequences. Some better algorithms were constructed to decrease the computational complexity but still on the level of O(n3) where nis the length of the RNA strings. The results of using the algorithms to the training sample consisted of tRNA chains of Acinetobacter sp. bactery are described.