Name
Title | ||
|---|---|---|
A max-margin training of RNA secondary structure prediction integrated with the thermodynamic model. |
Abstract | ||
|---|---|---|
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. 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 l(1) 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. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1142/S0219720018400255 | JOURNAL OF BIOINFORMATICS AND COMPUTATIONAL BIOLOGY |
Keywords | Field | DocType |
RNA secondary structure prediction,thermodynamic model,structured support vector machine | Computer science,Rna secondary structure prediction,Artificial intelligence,Generalization error,Structural risk minimization,Protein secondary structure,Thermodynamic model,Machine learning,Nucleic acid secondary structure,Minimum free energy | Journal |
Volume | Issue | ISSN |
16 | SP6 | 0219-7200 |
Citations | PageRank | References |
1 | 0.43 | 16 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Manato Akiyama | 1 | 1 | 0.43 |
| Kengo Sato | 2 | 392 | 22.46 |
| Yasubumi Sakakibara | 3 | 769 | 62.91 |