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djohk00787.pdf | Dissertation_全文 | 4.18 MB | Adobe PDF | View/Open |
yjohk00787.pdf | Abstract_要旨 | 179.4 kB | Adobe PDF | View/Open |
Title: | Design of Computational Models for Analyzing Graph-Structured Biological Data |
Other Titles: | グラフ構造をもつ生物情報データに対する計算モデルのデザイン |
Authors: | Wang, Feiqi |
Author's alias: | 王, 菲琪 |
Keywords: | Bioinformatics Computational model RNA secondary structure Protein kinase inhibitor Machine learning Artificial neural network Graph theory |
Issue Date: | 23-Mar-2022 |
Publisher: | Kyoto University |
Description: | 付記する学位プログラム名: デザイン学大学院連携プログラム |
Conferring University: | 京都大学 |
Degree Level: | 新制・課程博士 |
Degree Discipline: | 博士(情報学) |
Degree Report no.: | 甲第24031号 |
Degree no.: | 情博第787号 |
Conferral date: | 2022-03-23 |
Degree Call no.: | 新制||情||134(附属図書館) |
Degree Affiliation: | 京都大学大学院情報学研究科知能情報学専攻 |
Examination Committee members: | (主査)教授 阿久津 達也, 教授 山本 章博, 教授 鹿島 久嗣 |
Provisions of the Ruling of Degree: | 学位規則第4条第1項該当 |
Rights: | Comparison of pseudoknotted RNA secondary structures by topological centroid identification and tree edit distance. Journal of Computational Biology, 2020, 27(9), 1443-1451. Doi: 10.1089/cmb.2019.0512 A novel graph convolutional neural network for predicting interaction sites on protein kinase inhibitors in phosphorylation. Scientific reports, 2022, 12(1), 1-11. Doi: 10.1038/s41598-021-04230-7 |
DOI: | 10.14989/doctor.k24031 |
URI: | http://hdl.handle.net/2433/275353 |
Appears in Collections: | 140 Doctoral Dissertation (Informatics) |

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