{"created":"2025-01-19T00:35:55.288265+00:00","updated":"2025-01-20T10:59:02.646673+00:00","metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00164066","sets":["6504:8291:8753"]},"path":["8753"],"owner":"6748","recid":"164066","title":["深層学習に基づくタンパク質と化合物の相互作用予測"],"pubdate":{"attribute_name":"公開日","attribute_value":"2015-03-17"},"_buckets":{"deposit":"b7377611-b087-431f-bdba-dd55f169d0a8"},"_deposit":{"id":"164066","pid":{"type":"depid","value":"164066","revision_id":0},"owners":[6748],"status":"published","created_by":6748},"item_title":"深層学習に基づくタンパク質と化合物の相互作用予測","author_link":["322702","322699","322700","322701"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"深層学習に基づくタンパク質と化合物の相互作用予測"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"データとウェブ","subitem_subject_scheme":"Other"}]},"item_type_id":"22","publish_date":"2015-03-17","item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"jpn"}]},"item_22_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"京大"},{"subitem_text_value":"京大"},{"subitem_text_value":"京大"},{"subitem_text_value":"京大"}]},"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/164066/files/IPSJ-Z77-4B-07.pdf","label":"IPSJ-Z77-4B-07.pdf"},"date":[{"dateType":"Available","dateValue":"2016-06-10"}],"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-Z77-4B-07.pdf","filesize":[{"value":"543.7 kB"}],"mimetype":"application/pdf","priceinfo":[{"tax":["include_tax"],"price":"0","billingrole":"6"},{"tax":["include_tax"],"price":"0","billingrole":"8"},{"tax":["include_tax"],"price":"0","billingrole":"44"}],"accessrole":"open_date","version_id":"0d99d5c1-0ae6-4044-9e3b-89b376f9c142","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2015 by the Information Processing Society of Japan"}]},"item_22_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"浜中, 雅俊"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"種石, 慶"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Brown, J. B."}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"奥野, 恭史"}],"nameIdentifiers":[{}]}]},"item_resource_type":{"attribute_name":"資源タイプ","attribute_value_mlt":[{"resourceuri":"http://purl.org/coar/resource_type/c_5794","resourcetype":"conference paper"}]},"item_22_source_id_9":{"attribute_name":"書誌レコードID","attribute_value_mlt":[{"subitem_source_identifier":"AN00349328","subitem_source_identifier_type":"NCID"}]},"item_22_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"本稿では，医薬品の候補化合物の発見に用いられる，タンパク質とリガンド化合物の相互作用の予測について述べる．これまで我々は，活性が確かめられている12.5万件の結合データと，結合データに含まれない同数の組み合わせを非結合データとして用意し，その両者からなる学習データをサポートベクターマシンを用いて学習することで結合/非結合を予測する方法を提案してきた．しかし，サポートベクターマシンを用いた手法では，学習データが増えるにつれて学習時間が長大になることや，新たに少数の学習データが追加された場合でも再度学習をやりなおさなくてはならないなど，今後大規模な相互作用データを学習していく上で検討すべき課題があった．そこで本稿では，相互作用予測にDeep Lerningの一手法である，Deep Beleif Networksを用いることを検討する．","subitem_description_type":"Other"}]},"item_22_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"452","bibliographic_titles":[{"bibliographic_title":"第77回全国大会講演論文集"}],"bibliographicPageStart":"451","bibliographicIssueDates":{"bibliographicIssueDate":"2015-03-17","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"1","bibliographicVolumeNumber":"2015"}]},"relation_version_is_last":true,"weko_creator_id":"6748"},"id":164066,"links":{}}