{"id":210671,"metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00210671","sets":["581:10433:10437"]},"path":["10437"],"owner":"44499","recid":"210671","title":["Epitope Prediction of Antigen Protein Using Attention-based LSTM Network"],"pubdate":{"attribute_name":"公開日","attribute_value":"2021-04-15"},"_buckets":{"deposit":"48676c3d-1a90-4525-992b-dd1a36e21657"},"_deposit":{"id":"210671","pid":{"type":"depid","value":"210671","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"Epitope Prediction of Antigen Protein Using Attention-based LSTM Network","author_link":["533961","533951","533955","533957","533959","533952","533950","533958","533962","533954","533960","533953","533956","533949"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"Epitope Prediction of Antigen Protein Using Attention-based LSTM Network"},{"subitem_title":"Epitope Prediction of Antigen Protein Using Attention-based LSTM Network","subitem_title_language":"en"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"[一般論文] B-cell epitope prediction, protein, amino acid sequence, epitope, LSTM, attention","subitem_subject_scheme":"Other"}]},"item_type_id":"2","publish_date":"2021-04-15","item_2_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"Future Corporation"},{"subitem_text_value":"Future Corporation"},{"subitem_text_value":"Future Corporation"},{"subitem_text_value":"Future Corporation"},{"subitem_text_value":"FunPep Co., Ltd."},{"subitem_text_value":"FunPep Co., Ltd."},{"subitem_text_value":"Department of Health Development, Osaka University Graduate School of Medicine"}]},"item_2_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":"FunPep Co., Ltd.","subitem_text_language":"en"},{"subitem_text_value":"FunPep Co., Ltd.","subitem_text_language":"en"},{"subitem_text_value":"Department of Health Development, Osaka University Graduate School of Medicine","subitem_text_language":"en"}]},"item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"eng"}]},"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/210671/files/IPSJ-JNL6204019.pdf","label":"IPSJ-JNL6204019.pdf"},"date":[{"dateType":"Available","dateValue":"2023-04-15"}],"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-JNL6204019.pdf","filesize":[{"value":"823.2 kB"}],"mimetype":"application/pdf","priceinfo":[{"tax":["include_tax"],"price":"0","billingrole":"5"},{"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":"88788bf5-54bf-43a4-8294-0001e8f7bdf9","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2021 by the Information Processing Society of Japan"}]},"item_2_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Toshiaki, Noumi"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Seiichi, Inoue"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Haruka, Fujita"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Kugatsu, Sadamitsu"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Makoto, Sakaguchi"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Akiko, Tenma"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Hironori, Nakagami"}],"nameIdentifiers":[{}]}]},"item_2_creator_6":{"attribute_name":"著者名(英)","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Toshiaki, Noumi","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Seiichi, Inoue","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Haruka, Fujita","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Kugatsu, Sadamitsu","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Makoto, Sakaguchi","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Akiko, Tenma","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Hironori, Nakagami","creatorNameLang":"en"}],"nameIdentifiers":[{}]}]},"item_2_source_id_9":{"attribute_name":"書誌レコードID","attribute_value_mlt":[{"subitem_source_identifier":"AN00116647","subitem_source_identifier_type":"NCID"}]},"item_resource_type":{"attribute_name":"資源タイプ","attribute_value_mlt":[{"resourceuri":"http://purl.org/coar/resource_type/c_6501","resourcetype":"journal article"}]},"item_2_source_id_11":{"attribute_name":"ISSN","attribute_value_mlt":[{"subitem_source_identifier":"1882-7764","subitem_source_identifier_type":"ISSN"}]},"item_2_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"B-cells inducing antigen-specific immune responses in vivo produce large amounts of antigen-specific antibodies by recognizing the subregions (epitope regions) of antigen proteins. These antibodies can inhibit the functioning of antigen proteins. Predicting epitope regions is beneficial for the design and development of vaccines aimed to induce antigen-specific antibody production. However, prediction accuracy requires improvement. The conventional epitope region prediction methods have focused only on the target sequence in the amino acid sequences of an entire antigen protein and have not thoroughly considered its sequence and features as a whole. In the present paper, we propose a deep learning method based on long short-term memory with an attention mechanism to consider the characteristics of a whole antigen protein in addition to the target sequence. The proposed method achieves better accuracy compared with the conventional method in the experimental prediction of epitope regions using the data from the immune epitope database.\n------------------------------\nThis is a preprint of an article intended for publication Journal of\nInformation Processing(JIP). This preprint should not be cited. This\narticle should be cited as: Journal of Information Processing Vol.29(2021) (online)\nDOI　http://dx.doi.org/10.2197/ipsjjip.29.321\n------------------------------","subitem_description_type":"Other"}]},"item_2_description_8":{"attribute_name":"論文抄録(英)","attribute_value_mlt":[{"subitem_description":"B-cells inducing antigen-specific immune responses in vivo produce large amounts of antigen-specific antibodies by recognizing the subregions (epitope regions) of antigen proteins. These antibodies can inhibit the functioning of antigen proteins. Predicting epitope regions is beneficial for the design and development of vaccines aimed to induce antigen-specific antibody production. However, prediction accuracy requires improvement. The conventional epitope region prediction methods have focused only on the target sequence in the amino acid sequences of an entire antigen protein and have not thoroughly considered its sequence and features as a whole. In the present paper, we propose a deep learning method based on long short-term memory with an attention mechanism to consider the characteristics of a whole antigen protein in addition to the target sequence. The proposed method achieves better accuracy compared with the conventional method in the experimental prediction of epitope regions using the data from the immune epitope database.\n------------------------------\nThis is a preprint of an article intended for publication Journal of\nInformation Processing(JIP). This preprint should not be cited. This\narticle should be cited as: Journal of Information Processing Vol.29(2021) (online)\nDOI　http://dx.doi.org/10.2197/ipsjjip.29.321\n------------------------------","subitem_description_type":"Other"}]},"item_2_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographic_titles":[{"bibliographic_title":"情報処理学会論文誌"}],"bibliographicIssueDates":{"bibliographicIssueDate":"2021-04-15","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"4","bibliographicVolumeNumber":"62"}]},"relation_version_is_last":true,"weko_creator_id":"44499"},"updated":"2025-01-19T18:03:43.713786+00:00","created":"2025-01-19T01:11:52.621251+00:00","links":{}}