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KAKEN_16K00404seika.pdf
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Title |
次世代シークエンシングデータを利用した機械学習によるRNA二次構造予測の高精度化
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ジセダイ シークエンシング データ オ リヨウシタ キカイ ガクシュウ ニ ヨル RNA ニジ コウゾウ ヨソク ノ コウセイドカ
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Jisedai shīkuenshingu dēta o riyōshita kikai gakushū ni yoru RNA niji kōzō yosoku no kōseidoka
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Improving the accuracy of RNA secondary structure prediction by machine learning based on next-generation sequencing data
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佐藤, 健吾
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サトウ, ケンゴ
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Satō, Kengo
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慶應義塾大学・理工学部 (矢上) ・講師
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Research team head
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科研費研究者番号 : 20365472
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2020
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科学研究費補助金研究成果報告書
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2019
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部分的な構造情報である二次構造プロファイルを弱レベル学習データとして利用可能とする機械学習アルゴリズムを開発し,既存手法よりも精密な二次構造モデルを大量の二次構造プロファイルから学習することによって,過学習を回避しつつRNA二次構造予測の精度向上を目指す.まず,既存のTurner熱力学モデルに基づく自由エネルギー最小化法と構造化SVMによるパラメータ学習法を融合することによってより頑健かつ高精度なRNA二次構造予測手法の開発を行った.計算機実験の結果,既存の手法に見られる過学習は観測されず,予測精度の向上が確認された.
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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研究種目 : 基盤研究 (C) (一般)
研究期間 : 2016~2019
課題番号 : 16K00404
研究分野 : バイオインフォマティクス
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