Soft Computing based Model for Identification of Pseudoknots in RNA Sequence using Learning Grammar
نویسندگان
چکیده
RNA structure prediction is one of the major topics in bioinformatics. Among the various RNA structures, pseudoknots are the most complex and unique structure. Various methods have been used for modeling RNA pseudoknotted secondary structure. In this paper a new model for prediction of RNA pseudoknot structure has been proposed. In this model, features of two existing techniques, i. e. neural network and grammar are combined. The advantage of grammar, identification based on rules is combined with the strength of a neural network to learn. An Elman neural network is used to learn the context free grammar that represents a pseudoknot. This Learning grammar network further identifies if the RNA sequence contains pseudoknot or not. Learning grammar helps in reducing the drawbacks of both neural network and grammar thus increasing the overall power of identifying sequences with pseudoknots.
منابع مشابه
Identification of Pseudoknots in RNA Secondary Structures: A Grammatical Inference Approach
Grammatical inference, an important field of syntactic pattern recognition, is finding wider acceptance in many practical applications like computation biology. In this work we show the use of grammatical inference techniques in identifying pseudoknots in the RNA secondary structures. Identification of RNA secondary structure is among the few structure identification problems that can be solved...
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