{"updated":"2025-01-19T18:52:45.534266+00:00","metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00208241","sets":["1164:5352:10159:10426"]},"path":["10426"],"owner":"44499","recid":"208241","title":["大規模タンパク質データベースに基づくBERTを用いたペプチド結合予測"],"pubdate":{"attribute_name":"公開日","attribute_value":"2020-11-30"},"_buckets":{"deposit":"c0f39ec6-cd65-4973-8a33-69ac265f9a9a"},"_deposit":{"id":"208241","pid":{"type":"depid","value":"208241","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"大規模タンパク質データベースに基づくBERTを用いたペプチド結合予測","author_link":["521304","521305","521302","521303","521306","521300","521301","521307"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"大規模タンパク質データベースに基づくBERTを用いたペプチド結合予測"},{"subitem_title":"Prediction of peptide binding using BERT based on a large scale protein database","subitem_title_language":"en"}]},"item_type_id":"4","publish_date":"2020-11-30","item_4_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"フューチャー株式会社"},{"subitem_text_value":"フューチャー株式会社"},{"subitem_text_value":"フューチャー株式会社"},{"subitem_text_value":"フューチャー株式会社"},{"subitem_text_value":"フューチャー株式会社"},{"subitem_text_value":"株式会社ファンペップ"},{"subitem_text_value":"株式会社ファンペップ"},{"subitem_text_value":"大阪大学大学院"}]},"item_4_text_4":{"attribute_name":"著者所属(英)","attribute_value_mlt":[{"subitem_text_value":"Future Corporation","subitem_text_language":"en"},{"subitem_text_value":"Future Corporation","subitem_text_language":"en"},{"subitem_text_value":"Future Corporation","subitem_text_language":"en"},{"subitem_text_value":"Future Corporation","subitem_text_language":"en"},{"subitem_text_value":"Future Corporation","subitem_text_language":"en"},{"subitem_text_value":"FunPep Co., Ltd.","subitem_text_language":"en"},{"subitem_text_value":"FunPep Co., 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誠"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"天満, 昭子"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"中神, 啓徳"}],"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":"ワクチン開発において，B 細胞エピトープ予測と，MHCII に対するペプチドの結合予測はいずれも重要な予測タスクである．B 細胞エピトープを予測することは，抗原に特異的な抗体産生を誘導するワクチンの設計・開発のために有益である．一方，感染の重症度を低減する T 細胞を活性化するワクチン開発に対しても，MHCII に対するペプチドの結合を予測する必要がある．これら予測タスクに対する機械学習を用いた従来手法には，以下の二つの課題がある．一点目は離れたアミノ酸間の複雑な依存関係を捉えていない課題，二点目は学習データが不十分な場合に精度が低いという課題である．これらの課題に対処するために，本稿では大規模タンパク質データベースにより事前学習した，自己注意機構を持つ BERT モデルを用いた手法を提案する．実験の結果，提案手法は B 細胞エピトープ予測，MHCII に対するペプチドの結合予測の実験で従来よりも高い性能を達成した．","subitem_description_type":"Other"}]},"item_4_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"8","bibliographic_titles":[{"bibliographic_title":"研究報告バイオ情報学（BIO）"}],"bibliographicPageStart":"1","bibliographicIssueDates":{"bibliographicIssueDate":"2020-11-30","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"1","bibliographicVolumeNumber":"2020-BIO-64"}]},"relation_version_is_last":true,"weko_creator_id":"44499"},"created":"2025-01-19T01:09:48.826943+00:00","id":208241,"links":{}}