TY - GEN

T1 - The composition of dense neural networks and formal grammars for secondary structure analysis

AU - Grigorev, Semyon

AU - Lunina, Polina

PY - 2019/1/1

Y1 - 2019/1/1

N2 - 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 processing which shows the applicability of our idea to real problems.

AB - 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 processing which shows the applicability of our idea to real problems.

KW - Dense Neural Network

KW - DNN

KW - Formal Grammars

KW - Genomic Sequences

KW - Machine Learning

KW - Parsing

KW - Proteomic Sequences

KW - Secondary Structure

UR - http://www.scopus.com/inward/record.url?scp=85064687958&partnerID=8YFLogxK

M3 - Conference contribution

AN - SCOPUS:85064687958

SP - 234

EP - 241

BT - BIOINFORMATICS 2019 - 10th International Conference on Bioinformatics Models, Methods and Algorithms, Proceedings; Part of 12th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2019

A2 - De Maria, Elisabetta

A2 - Gamboa, Hugo

A2 - Fred, Ana

PB - SciTePress

T2 - 10th International Conference on Bioinformatics Models, Methods and Algorithms, BIOINFORMATICS 2019 - Part of 12th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2019

Y2 - 22 February 2019 through 24 February 2019

ER -