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 Applying Machine Learning To Rna Secondary Structure Prediction Problem
Tác giả hoặc Nhóm tác giả: Doan Duy Binh, Pham Minh Tuan, Dang Duc Long
Nơi đăng: Proceedings of the Conference on Information Technology and its Applications (CITA); Số: CITA 2023;Từ->đến trang: 33-44;Năm: 2023
Lĩnh vực: Công nghệ thông tin; Loại: Bài báo khoa học; Thể loại: Trong nước
TÓM TẮT
RNA molecules play important roles, including catalysis of chemical reactions and control of gene expression, and their functions largely depend on their folded structures. Since determining these structures by biochemical means is expensive, there is increased demand for computational predictions of RNA structures. In recent years, machine learning techniques have emerged as promising tools for RNA secondary structure prediction, offering improved performance and faster processing times. In this paper, we propose an approach that applies machine learning algorithms to the RNA secondary structure prediction problem. We leverage a large dataset of known RNA secondary structures to train and evaluate different machine learning models. The dataset consists of experimentally determined structures and incorporates diverse RNA sequences and structural elements. The proposed method leverages the power of deep learning models, specifically deep learning, to capture the intricate relationships between RNA sequences and their corresponding secondary structures. This paper demonstrates the successful application of machine learning techniques, specifically deep learning models, to the challenging problem of RNA secondary structure prediction. The proposed approach achieves improved accuracy and holds great potential for advancing our understanding of RNA biology and facilitating the design of therapeutic interventions targeting RNA molecules.
ABSTRACT
RNA molecules play important roles, including catalysis of chemical reactions and control of gene expression, and their functions largely depend on their folded structures. Since determining these structures by biochemical means is expensive, there is increased demand for computational predictions of RNA structures. In recent years, machine learning techniques have emerged as promising tools for RNA secondary structure prediction, offering improved performance and faster processing times. In this paper, we propose an approach that applies machine learning algorithms to the RNA secondary structure prediction problem. We leverage a large dataset of known RNA secondary structures to train and evaluate different machine learning models. The dataset consists of experimentally determined structures and incorporates diverse RNA sequences and structural elements. The proposed method leverages the power of deep learning models, specifically deep learning, to capture the intricate relationships between RNA sequences and their corresponding secondary structures. This paper demonstrates the successful application of machine learning techniques, specifically deep learning models, to the challenging problem of RNA secondary structure prediction. The proposed approach achieves improved accuracy and holds great potential for advancing our understanding of RNA biology and facilitating the design of therapeutic interventions targeting RNA molecules.
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