{"links":{},"metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00225239","sets":["1164:5352:11207:11208"]},"path":["11208"],"owner":"44499","recid":"225239","title":["親和性閾値を考慮した適用領域による薬剤標的親和性予測"],"pubdate":{"attribute_name":"公開日","attribute_value":"2023-03-02"},"_buckets":{"deposit":"be2d505b-22f3-460f-b3cc-05c3f4157e4c"},"_deposit":{"id":"225239","pid":{"type":"depid","value":"225239","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"親和性閾値を考慮した適用領域による薬剤標的親和性予測","author_link":["595617","595618"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"親和性閾値を考慮した適用領域による薬剤標的親和性予測"}]},"item_type_id":"4","publish_date":"2023-03-02","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_4_text_4":{"attribute_name":"著者所属(英)","attribute_value_mlt":[{"subitem_text_value":"Department of Computer Science, School of Computing, Tokyo Institute of Technology","subitem_text_language":"en"},{"subitem_text_value":"Department of Computer Science, School of Computing, Tokyo Institute of Technology","subitem_text_language":"en"}]},"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/225239/files/IPSJ-BIO23073016.pdf","label":"IPSJ-BIO23073016.pdf"},"date":[{"dateType":"Available","dateValue":"2025-03-02"}],"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-BIO23073016.pdf","filesize":[{"value":"880.9 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":"73d0963e-1d08-4825-ad75-8d73e884c84a","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2023 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":"薬剤標的親和性の計算機による予測は創薬のコストを削減するため，喫緊の課題である．新規の相互作用の同定のためには，大きな誤差を持つ予測は可能な限り排除するべきである．機械学習の分野では，誤差が大きくなりやすいものを取り除く適用領域と呼ばれる手法が用いられてきた．しかし，ケモゲノミクス手法において，適用領域の利用の研究は限られている．本論文では，薬剤標的親和性予測に適用領域を利用し高い精度による回帰予測と，高い活性を持つ薬剤標的ペアの分類を両立し，適用領域におけるリジェクトオプションが高い活性を持つ薬剤標的ペアを排除することを防ぐ新たなフレームワークを提案する．","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":"2023-03-02","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"16","bibliographicVolumeNumber":"2023-BIO-73"}]},"relation_version_is_last":true,"weko_creator_id":"44499"},"created":"2025-01-19T01:24:45.069596+00:00","updated":"2025-01-19T12:52:17.112389+00:00","id":225239}