scholarly journals Learning to Fold RNAs in Linear Time

2019 ◽  
Author(s):  
F A Rezaur Rahman Chowdhury ◽  
He Zhang ◽  
Liang Huang
Keyword(s):  
Machine Learning ◽  
Secondary Structure ◽  
Thermodynamic Parameters ◽  
Rna Secondary Structure ◽  
Linear Time ◽  
Training Algorithm ◽  
Time Machine ◽  
Experimental Limitation ◽  
Prediction Systems ◽  
Update Strategy

AbstractRNA secondary structure is helpful for understanding RNA’s functionality, thus accurate prediction systems are desired. Both thermodynamics-based models and machine learning-based models have been used in different prediction systems to solve this problem. Compared to thermodynamics-based models, machine learning-based models can address the inaccurate measurement of thermodynamic parameters due to experimental limitation. However, the existing methods for training machine learning-based models are still expensive because of their cubic-time inference cost. To overcome this, we present a linear-time machine learning-based folding system, using recently proposed approximate folding tool LinearFold as inference engine, and structured SVM (sSVM) as training algorithm. Furthermore, to remedy non-convergence of naive sSVM with inexact search inference, we introduce a max violation update strategy. The training speed of our system is 41× faster than CONTRAfold on a diverse dataset for one epoch, and 14× faster than MXfold on a dataset with longer sequences. With the learned parameters, our system improves the accuracy of LinearFold, and is also the most accurate system among selected folding tools, including CONTRAfold, Vienna RNAfold and MXfold.

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

2017 ◽  
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]

Expanded sequence dependence of thermodynamic parameters improves prediction of RNA secondary structure

1999 ◽  
Vol 288 (5) ◽  
pp. 911-940 ◽  
Author(s):  
David H. Mathews ◽  
Jeffrey Sabina ◽  
Michael Zuker ◽  
Douglas H. Turner
Keyword(s):  
Secondary Structure ◽  
Thermodynamic Parameters ◽  
Rna Secondary Structure ◽  
Sequence Dependence

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

2018 ◽  
Vol 16 (06) ◽  
pp. 1840025 ◽  
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 .

scholarly journals Review of machine learning methods for RNA secondary structure prediction

2021 ◽  
Vol 17 (8) ◽  
pp. e1009291
Author(s):  
Qi Zhao ◽  
Zheng Zhao ◽  
Xiaoya Fan ◽  
Zhengwei Yuan ◽  
Qian Mao ◽  
...  
Keyword(s):  
Machine Learning ◽  
Secondary Structure ◽  
Structure Prediction ◽  
Rna Secondary Structure ◽  
Secondary Structure Prediction ◽  
Computational Prediction ◽  
Prediction Methods ◽  
Comprehensive Overview ◽  
Rna Secondary Structure Prediction ◽  
New Methods

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.

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