機械学習と混合整数線形計画法に基づく新しい化合物推定手法

Persistent ID (NDL): info:ndljp/pid/13120194

Material type: 博士論文

Author: 朱, 見深

Publisher: Kyoto University

Date granted: 2023-09-25

Material Format: Digital

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Degree grantor and degree: 京都大学,Kyoto University,博士(情報学)

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Note (General)：: 機関リポジトリ記載の権利情報: Chapter 5 is adapted from [Zhu et al., A novel method for inferring chemical compounds with prescribed topological substructures based...

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Material Type: 博士論文
Title: 機械学習と混合整数線形計画法に基づく新しい化合物推定手法
Author/Editor: 朱, 見深
Author Heading: 朱, 見深
Publication, Distribution, etc.: Kyoto University
Publication Date: 2023-09-25
Publication Date (W3CDTF): 2023-09-25
Alternative Title: Novel Methods for Chemical Compound Inference Based on Machine Learning and Mixed Integer Linear Programming
Contributor: 原口, 和也
山下, 信雄
阿久津, 達也
Degree Grantor: 京都大学
Kyoto University
Date Granted: 2023-09-25
Date Granted (W3CDTF): 2023-09-25
Dissertation Number: 甲第24938号
Degree Type: 博士(情報学)
Text Language Code: eng
Subject Heading: Inverse QSAR
Machine Learning
Mixed Integer Linear Programming
Chemical Graphs
NDC: 007
Target Audience: 一般
Note (General): 機関リポジトリ記載の権利情報: Chapter 5 is adapted from [Zhu et al., A novel method for inferring chemical compounds with prescribed topological substructures based on integer programming. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 19(6):3233–3245, 2021], https://doi.org/10.1109/TCBB.2021.3112598, Copyright ©︎IEEE 2021. In reference to IEEE copyrighted material which is used with permission in this thesis, the IEEE does not endorse any of Kyoto University's products or services. Internal or personal use of this material is permitted. If interested in reprinting/republishing IEEE copyrighted material for advertising or promotional purposes or for creating new collective works for resale or redistribution, please go to http://www.ieee.org/publications_standards/publications/rights/rights_link.html to learn how to obtain a License from RightsLink. If applicable, University Microfilms and/or ProQuest Library, or the Archives of Canada may supply single copies of the dissertation. Chapter 7 is adapted from [Zhu et al., Adjustive linear regression and its application to the inverse QSAR. In Proceedings of the 15th International Joint Conference on Biomedical Engineering Systems and Technologies - BIOINFORMATICS, pages 144–151. INSTICC, SciTePress, 2022], https://doi.org/10.5220/0010853700003123, with permission from the publisher. Copyright ©︎SCITEPRESS 2022. Chapters 4, 6, and 8 are adapted from https://doi.org/10.3390/a13050124, https://doi.org/10.31083/j.fbl2706188, and https://doi.org/10.48550/arXiv.2209.13527, respectively, and have been reproduced with the permission of the publisher under CC-BY license.
DOI: 10.14989/doctor.k24938
https://doi.org/10.14989/doctor.k24938
Persistent ID (NDL): info:ndljp/pid/13120194
https://dl.ndl.go.jp/pid/13120194
Collection: 障害者向け資料
Collection (Materials For Handicapped People:1): テキストデータ
Collection (particular): 国立国会図書館デジタルコレクション > デジタル化資料 > 博士論文
https://dl.ndl.go.jp/collections/A00014
Acquisition Basis: 博士論文（自動収集）
Date Accepted (W3CDTF): 2023-12-05T21:41:07+09:00
Format (IMT): application/pdf
Access Restrictions: 国立国会図書館内限定公開
Service for the Digitized Contents Transmission Service: 図書館・個人送信対象外
Availability of remote photoduplication service: 可
Periodical Title (URI): https://doi.org/10.14989/doctor.k24938
http://hdl.handle.net/2433/285872
Data Provider (Database): 国立国会図書館 : 国立国会図書館デジタルコレクション
https://dl.ndl.go.jp