| Budget Amount *help |
¥4,550,000 (Direct Cost: ¥3,500,000、Indirect Cost: ¥1,050,000)
Fiscal Year 2018: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000)
Fiscal Year 2017: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000)
Fiscal Year 2016: ¥1,690,000 (Direct Cost: ¥1,300,000、Indirect Cost: ¥390,000)
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| Outline of Final Research Achievements |
We have developed a machine learning algorithm that makes it possible to use secondary structure profiles, which are partial structural information, as weak-level learning data, and aims to improve the accuracy of RNA secondary structure prediction without overfitting by learning a large number of secondary structure models that are more precise than existing methods. First, we developed a more robust and accurate method for RNA secondary structure prediction by integrating the free energy minimization method based on the existing Turner thermodynamic model with the machine learning method using a structured SVM. The results of the computer experiments showed that no overfitting was observed, unlike in the existing methods, and the prediction accuracy was improved.
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