Исследовательская группа

Лаборатория языковых инструментов

Анализатор вторичной структуры цепочек

Григорьев СемёнАктивный

The composition of formal grammars and artificial neural networks for secondary structure analysis.

We propose a way to combine formal grammars and artificial neural networks for secondary structure processing. Formal grammars encode the secondary structure of the sequence and neural networks deal with patterns detection and noise.

This approach can be applied for different types of sequences which have rich secondary structure.

Currently we are working on application of this approach for biological sequences analysis (RNA, proteins). 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 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 neural network to process features.

Участники

Лунина Полина
Григорьев Семён

Материалы

Публикации

Improved Architecture of Artificial Neural Network for Secondary Structure Analysis

November 2019

Semyon Grigorev and Polina Lunina

Подробнее

The Composition of Dense Neural Networks and Formal Grammars for Secondary Structure Analysis

March 2019

Semyon Grigorev and Polina Lunina

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 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.

Подробнее