{"created":"2025-01-19T01:17:25.543061+00:00","updated":"2025-01-19T15:41:20.857677+00:00","metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00216914","sets":["1164:5352:10882:10883"]},"path":["10883"],"owner":"44499","recid":"216914","title":["適用領域を考慮した薬剤標的親和性予測"],"pubdate":{"attribute_name":"公開日","attribute_value":"2022-03-03"},"_buckets":{"deposit":"26c7217a-c0d4-4d35-984c-a036bfa7c1ac"},"_deposit":{"id":"216914","pid":{"type":"depid","value":"216914","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"適用領域を考慮した薬剤標的親和性予測","author_link":["560919","560920"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"適用領域を考慮した薬剤標的親和性予測"}]},"item_type_id":"4","publish_date":"2022-03-03","item_4_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"東京工業大学情報理工学院情報工学系"},{"subitem_text_value":"東京工業大学情報理工学院情報工学系"}]},"item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"jpn"}]},"item_publisher":{"attribute_name":"出版者","attribute_value_mlt":[{"subitem_publisher":"情報処理学会","subitem_publisher_language":"ja"}]},"publish_status":"0","weko_shared_id":-1,"item_file_price":{"attribute_name":"Billing file","attribute_type":"file","attribute_value_mlt":[{"url":{"url":"https://ipsj.ixsq.nii.ac.jp/record/216914/files/IPSJ-BIO22069004.pdf","label":"IPSJ-BIO22069004.pdf"},"date":[{"dateType":"Available","dateValue":"2024-03-03"}],"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-BIO22069004.pdf","filesize":[{"value":"812.1 kB"}],"mimetype":"application/pdf","priceinfo":[{"tax":["include_tax"],"price":"660","billingrole":"5"},{"tax":["include_tax"],"price":"330","billingrole":"6"},{"tax":["include_tax"],"price":"0","billingrole":"41"},{"tax":["include_tax"],"price":"0","billingrole":"44"}],"accessrole":"open_date","version_id":"a968331f-8bc1-4790-b05c-88c489ca5737","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2022 by the Information Processing Society of Japan"}]},"item_4_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"杉田, 駿也"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"大上, 雅史"}],"nameIdentifiers":[{}]}]},"item_4_source_id_9":{"attribute_name":"書誌レコードID","attribute_value_mlt":[{"subitem_source_identifier":"AA12055912","subitem_source_identifier_type":"NCID"}]},"item_4_textarea_12":{"attribute_name":"Notice","attribute_value_mlt":[{"subitem_textarea_value":"SIG Technical Reports are nonrefereed and hence may later appear in any journals, conferences, symposia, etc."}]},"item_resource_type":{"attribute_name":"資源タイプ","attribute_value_mlt":[{"resourceuri":"http://purl.org/coar/resource_type/c_18gh","resourcetype":"technical report"}]},"item_4_source_id_11":{"attribute_name":"ISSN","attribute_value_mlt":[{"subitem_source_identifier":"2188-8590","subitem_source_identifier_type":"ISSN"}]},"item_4_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"計算機による創薬支援のため，化合物とタンパク質の親和性を網羅的に予測する薬剤標的親和性予測が行われている．化合物-タンパク質間の相互作用の既知情報，化合物の構造情報，タンパク質の配列情報などを用いた教師あり学習に基づく予測手法が，これまでに数多く報告されてきた．しかし，化合物や標的分子の化学空間は広範であるため，常に予測が上手くいくわけではない．本研究では薬剤標的親和性予測において予測モデルの出力の信頼性を評価する適用領域を用いて予測結果を分類する手法を提案する．薬剤標的親和性予測における最新手法である勾配ブースティング木とグラフ畳込みニューラルネットワークに基づく予測手法で検証した結果，構築した適用領域が実際に回帰予測精度を改善することを示した．","subitem_description_type":"Other"}]},"item_4_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"6","bibliographic_titles":[{"bibliographic_title":"研究報告バイオ情報学（BIO）"}],"bibliographicPageStart":"1","bibliographicIssueDates":{"bibliographicIssueDate":"2022-03-03","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"4","bibliographicVolumeNumber":"2022-BIO-69"}]},"relation_version_is_last":true,"weko_creator_id":"44499"},"id":216914,"links":{}}