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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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