What are the best machine learning methods to predict the RNA secondary structure?
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Machine learning (ML) methods for predicting RNA secondary structure have been extensively studied. In a review comparing 15 methods, it was found that deep learning (DL) algorithms, such as SPOT-RNA and UFold, outperformed shallow learning (SL) and traditional methods when the data distribution was similar in the training and testing set . However, when predicting 2D structures for new RNA families, DL methods did not show a clear advantage and their performance was comparable to SL and non-ML methods . Another study introduced DRfold, a method that uses deep end-to-end learning and a hybrid energy potential to predict RNA tertiary structures with significant improvements over previous approaches . Additionally, a deep learning model called RNAGCN, based on a Graph Convolutional Network (GCN), showed promising results in RNA tertiary structure assessment .
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2 Citations | The paper does not mention the best machine learning methods to predict RNA secondary structure. |
12 Sep 2022 | The paper proposes a deep learning model called RNA-par for predicting RNA secondary structure by partitioning the sequence into fragments based on exterior loops. |
| The paper does not mention the best machine learning methods for predicting RNA secondary structure. | |
| The paper does not mention the best machine learning methods for predicting RNA secondary structure. | |
| The review compares 15 methods for predicting RNA secondary structure, with deep learning algorithms like SPOT-RNA and UFold performing well. |
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