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- W1578767825 abstract "Studying the structure of RNA sequences is an important problem that helps in understanding the functional properties of RNA. Pseudoknot is one type of RNA structures that cannot be modeled with Context Free Grammars (CFG) because it exhibits crossing dependencies. Pseudoknot structures have functional importance since they appear, for example, in viral genome RNAs and ribozyme active sites. Tree Adjoining Grammars (TAG) is one example of a grammatical model that is more expressive than CFG and has the capability of dealing with crossing dependencies. In this paper, we describe a new inference algorithm for TAG RNA, a sub-model of TAG. We also introduce an RNA structure identification framework, TAG RNA Inf, within which the TAG RNA inference algorithm constitutes the core of the training phase. We present the results of using the proposed framework for identifying RNA sequences with pseudoknot structures. Our results outperform those reported in [14] for the same problem that employs a different grammatical formalism." @default.
- W1578767825 created "2016-06-24" @default.
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- W1578767825 date "2008-01-01" @default.
- W1578767825 modified "2023-09-23" @default.
- W1578767825 title "Pseudoknot Identification through Learning TAGRNA" @default.
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- W1578767825 doi "https://doi.org/10.1007/978-3-540-88436-1_12" @default.
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