loading
Documents

Research.Publish.Connect.

Paper

Authors: Semyon Grigorev and Polina Lunina

Affiliation: St. Petersburg State University, 7/9 Universitetskaya nab., St.Petersburg, Russia, JetBrains Research, Universitetskaya emb., 7-9-11/5A, St.Petersburg, Russia

ISBN: 978-989-758-353-7

Keyword(s): Dense Neural Network, DNN, Machine Learning, Secondary Structure, Genomic Sequences, Proteomic Sequences, Formal Grammars, Parsing.

Abstract: We propose a way to combine formal grammars and artificial neural networks for biological sequences processing. Formal grammars encode the secondary structure of the sequence and neural networks deal with mutations and noise. In contrast to the classical way, when probabilistic grammars are used for secondary structure modeling, we propose to use arbitrary (not probabilistic) grammars which simplifies grammar creation. Instead of modeling the structure of the whole sequence, we create a grammar which only describes features of the secondary structure. Then we use undirected matrix-based parsing to extract features: the fact that some substring can be derived from some nonterminal is a feature. After that, we use a dense neural network to process features. In this paper, we describe in details all the parts of our receipt: a grammar, parsing algorithm, and network architecture. We discuss possible improvements and future work. Finally, we provide the results of tRNA and 16s rRNA proces sing which shows the applicability of our idea to real problems. (More)

PDF ImageFull Text

Download
Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 54.236.246.85

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

Paper citation in several formats:
Grigorev, S. and Lunina, P. (2019). The Composition of Dense Neural Networks and Formal Grammars for Secondary Structure Analysis.In Proceedings of the 12th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 3: BIOINFORMATICS, ISBN 978-989-758-353-7, pages 234-241. DOI: 10.5220/0007472302340241

@conference{bioinformatics19,
author={Semyon Grigorev. and Polina Lunina.},
title={The Composition of Dense Neural Networks and Formal Grammars for Secondary Structure Analysis},
booktitle={Proceedings of the 12th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 3: BIOINFORMATICS,},
year={2019},
pages={234-241},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007472302340241},
isbn={978-989-758-353-7},
}

TY - CONF

JO - Proceedings of the 12th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 3: BIOINFORMATICS,
TI - The Composition of Dense Neural Networks and Formal Grammars for Secondary Structure Analysis
SN - 978-989-758-353-7
AU - Grigorev, S.
AU - Lunina, P.
PY - 2019
SP - 234
EP - 241
DO - 10.5220/0007472302340241

Login or register to post comments.

Comments on this Paper: Be the first to review this paper.