{"id":214308,"created":"2025-01-19T01:15:07.867658+00:00","metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00214308","sets":["1164:2735:10526:10756"]},"path":["10756"],"owner":"44499","recid":"214308","title":["Transformer及び既存BERTモデルを用いたRNA-蛋白の結合予測"],"pubdate":{"attribute_name":"公開日","attribute_value":"2021-12-06"},"_buckets":{"deposit":"b413293b-8d28-4ad2-91ff-d5562c94c0c0"},"_deposit":{"id":"214308","pid":{"type":"depid","value":"214308","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"Transformer及び既存BERTモデルを用いたRNA-蛋白の結合予測","author_link":["549460","549461","549459"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"Transformer及び既存BERTモデルを用いたRNA-蛋白の結合予測"},{"subitem_title":"RNA-protein Binding Prediction with Transformer and the Two Existing BERT Models","subitem_title_language":"en"}]},"item_type_id":"4","publish_date":"2021-12-06","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"},{"subitem_text_value":"東京工業大学物質・情報卓越教育院"},{"subitem_text_value":"東京工業大学情報理工学院"}]},"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/214308/files/IPSJ-MPS21136011.pdf","label":"IPSJ-MPS21136011.pdf"},"date":[{"dateType":"Available","dateValue":"2023-12-06"}],"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-MPS21136011.pdf","filesize":[{"value":"1.1 MB"}],"mimetype":"application/pdf","priceinfo":[{"tax":["include_tax"],"price":"660","billingrole":"5"},{"tax":["include_tax"],"price":"330","billingrole":"6"},{"tax":["include_tax"],"price":"0","billingrole":"17"},{"tax":["include_tax"],"price":"0","billingrole":"44"}],"accessrole":"open_date","version_id":"dc08ae99-b8cd-4683-b644-d5b4910d958f","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2021 by the Information Processing Society of Japan"}]},"item_4_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"木村, 高幸"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"安尾, 信明"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"関嶋, 政和"}],"nameIdentifiers":[{}]}]},"item_4_source_id_9":{"attribute_name":"書誌レコードID","attribute_value_mlt":[{"subitem_source_identifier":"AN10505667","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":"2188-8833","subitem_source_identifier_type":"ISSN"}]},"item_4_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"RNA 蛋白質の結合は，生体内で重要な鍵となる相互作用であるが，実験でのコスト等からコンピュータによる予測が求められている．既に多くのモデルが報告されているが，精度や評価の点で改善の余地がある．自然言語処理の分野等で Transformer が大きな成果を出しているが，RNA 蛋白質の結合予測にはまだ応用されていない．本研究では，この Transformer を利用して，RNA 蛋白質の結合予測モデルの構築を行う．","subitem_description_type":"Other"}]},"item_4_description_8":{"attribute_name":"論文抄録(英)","attribute_value_mlt":[{"subitem_description":"Bindings between RNA and protein are essential interactions in organisms. However, experimental approaches are relatively expensive. This is why inexpensive computational approaches are expected. A lot of models were already reported, but there is room for improvement in terms of AUROC and the evaluation methods. By the way, attention-based architecture called Transformer has been successful in many fields such as the natural language processing field, but the Transformer was not directly used to this RNA-protein binding prediction problems. In this study, we build a Transformer model to solve RNA-protein binding prediction problem especially for proteins that have no binding data to RNA.","subitem_description_type":"Other"}]},"item_4_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"4","bibliographic_titles":[{"bibliographic_title":"研究報告数理モデル化と問題解決（MPS）"}],"bibliographicPageStart":"1","bibliographicIssueDates":{"bibliographicIssueDate":"2021-12-06","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"11","bibliographicVolumeNumber":"2021-MPS-136"}]},"relation_version_is_last":true,"weko_creator_id":"44499"},"updated":"2025-01-19T16:49:40.618552+00:00","links":{}}