Abstract
A way to combine formal grammars and artificial neural networks for biological sequences processing was recently proposed. In this approach, an ordinary grammar encodes primitive features of the RNA secondary structure, parsing is utilized for features extraction and artificial neural network—for processing of the extracted features. Parsing is a bottleneck of the solution: input sequences should first be parsed before processing with a trained model which is a time-consuming operation when working with huge biological databases. In this work, we solve this problem by employing staged learning and limiting parsing to be used only during network training. We also compare networks which represent the parsing result in two different ways: by a vector and a bitmap image. Finally, we evaluate our solution on tRNA classification tasks.
Supported by the Russian Science Foundation grant 18-11-00100.
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Notes
- 1.
GtRNAdb tRNA database Web page: http://gtrnadb.ucsc.edu/. Access date: 05.06.2019.
- 2.
The tRNADB-CE tRNA database Web page: http://trna.ie.niigata-u.ac.jp/cgi-bin/trnadb/index.cgi. Access date: 05.06.2019.
- 3.
YaccConstructor is an SDK for syntax analysis tools development. Project repository on GitHub: https://github.com/YaccConstructor/YaccConstructor. Access date: 07.03.2020.
- 4.
Project description is available at the project page: https://research.jetbrains.org/groups/plt_lab/projects?project_id=43. Source code and documentation are published at GitHub: https://github.com/LuninaPolina/SecondaryStructureAnalyzer. Access date: 07.03.2020.
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Lunina, P., Grigorev, S. (2020). On Secondary Structure Analysis by Using Formal Grammars and Artificial Neural Networks. In: Cazzaniga, P., Besozzi, D., Merelli, I., Manzoni, L. (eds) Computational Intelligence Methods for Bioinformatics and Biostatistics. CIBB 2019. Lecture Notes in Computer Science(), vol 12313. Springer, Cham. https://doi.org/10.1007/978-3-030-63061-4_18
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