Review of machine learning methods for RNA secondary structure prediction

Secondary structure plays an important role in determining the function of noncoding RNAs. Hence, identifying RNA secondary structures is of great value to research. Computational prediction is a mainstream approach for predicting RNA secondary structure. Unfortunately, even though new methods have been proposed over the past 40 years, the performance of computational prediction methods has stagnated in the last decade. Recently, with the increasing availability of RNA structure data, new methods based on machine learning (ML) technologies, especially deep learning, have alleviated the issue. In this review, we provide a comprehensive overview of RNA secondary structure prediction methods based on ML technologies and a tabularized summary of the most important methods in this field. The current pending challenges in the field of RNA secondary structure prediction and future trends are also discussed.

Download Full-text

A max-margin training of RNA secondary structure prediction integrated with the thermodynamic model

10.1101/205047 ◽

2017 ◽

Cited By ~ 1

Author(s):

Manato Akiyama ◽

Kengo Sato ◽

Yasubumi Sakakibara

Keyword(s):

Machine Learning ◽

Secondary Structure ◽

Structure Prediction ◽

Rna Secondary Structure ◽

Prediction Accuracy ◽

Secondary Structure Prediction ◽

Training Data ◽

Support Vector ◽

Rna Secondary Structure Prediction ◽

Fine Grained

AbstractMotivation: A popular approach for predicting RNA secondary structure is the thermodynamic nearest neighbor model that finds a thermodynamically most stable secondary structure with the minimum free energy (MFE). For further improvement, an alternative approach that is based on machine learning techniques has been developed. The machine learning based approach can employ a fine-grained model that includes much richer feature representations with the ability to fit the training data. Although a machine learning based fine-grained model achieved extremely high performance in prediction accuracy, a possibility of the risk of overfitting for such model has been reported.Results: In this paper, we propose a novel algorithm for RNA secondary structure prediction that integrates the thermodynamic approach and the machine learning based weighted approach. Ourfine-grained model combines the experimentally determined thermodynamic parameters with a large number of scoring parameters for detailed contexts of features that are trained by the structured support vector machine (SSVM) with the ℓ1 regularization to avoid overfitting. Our benchmark shows that our algorithm achieves the best prediction accuracy compared with existing methods, and heavy overfitting cannot be observed.Availability: The implementation of our algorithm is available at https://github.com/keio-bioinformatics/mxfold.Contact:[email protected]

Download Full-text

A max-margin training of RNA secondary structure prediction integrated with the thermodynamic model

Journal of Bioinformatics and Computational Biology ◽

10.1142/s0219720018400255 ◽

2018 ◽

Vol 16 (06) ◽

pp. 1840025 ◽

Cited By ~ 5

Author(s):

Manato Akiyama ◽

Kengo Sato ◽

Yasubumi Sakakibara

Keyword(s):

Machine Learning ◽

Secondary Structure ◽

Structure Prediction ◽

Rna Secondary Structure ◽

Prediction Accuracy ◽

Secondary Structure Prediction ◽

Training Data ◽

Support Vector ◽

Rna Secondary Structure Prediction ◽

Fine Grained

A popular approach for predicting RNA secondary structure is the thermodynamic nearest-neighbor model that finds a thermodynamically most stable secondary structure with minimum free energy (MFE). For further improvement, an alternative approach that is based on machine learning techniques has been developed. The machine learning-based approach can employ a fine-grained model that includes much richer feature representations with the ability to fit the training data. Although a machine learning-based fine-grained model achieved extremely high performance in prediction accuracy, a possibility of the risk of overfitting for such a model has been reported. In this paper, we propose a novel algorithm for RNA secondary structure prediction that integrates the thermodynamic approach and the machine learning-based weighted approach. Our fine-grained model combines the experimentally determined thermodynamic parameters with a large number of scoring parameters for detailed contexts of features that are trained by the structured support vector machine (SSVM) with the [Formula: see text] regularization to avoid overfitting. Our benchmark shows that our algorithm achieves the best prediction accuracy compared with existing methods, and heavy overfitting cannot be observed. The implementation of our algorithm is available at https://github.com/keio-bioinformatics/mxfold .

Download Full-text