{"created":"2023-05-15T12:00:36.952344+00:00","id":7503,"links":{},"metadata":{"_buckets":{"deposit":"55089b79-3453-4619-9c39-fb124c30499e"},"_deposit":{"created_by":3,"id":"7503","owners":[3],"pid":{"revision_id":0,"type":"depid","value":"7503"},"status":"published"},"_oai":{"id":"oai:kyutech.repo.nii.ac.jp:00007503","sets":["8:24"]},"author_link":["7216","32444","32445","32448","32447"],"control_number":"7503","item_21_biblio_info_6":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicIssueDates":{"bibliographicIssueDate":"2020-12-30","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"3","bibliographicPageEnd":"108","bibliographicPageStart":"104","bibliographicVolumeNumber":"55","bibliographic_titles":[{"bibliographic_title":"顕微鏡","bibliographic_titleLang":"ja"}]}]},"item_21_description_4":{"attribute_name":"抄録","attribute_value_mlt":[{"subitem_description":"クライオ電子顕微鏡による単粒子解析では，試料中に含まれる複数のタンパク質構造を分類しながら解くことが出来る．ただし分類された構造間のダイナミクスの情報は類推するしかない．この問題について2020年に発表された三次元再構成およびクラス分類を行うための深層学習アプローチであるcryoDRGNは，離散的なデータ分割による構造分類を脱却し，連続的な構造分類を実現した．そこでは，オートエンコーダーをベースとし，入力粒子画像から投影パラメーターに依存する情報を分離して潜在空間を構築している．本稿では従来の構造分類と，cryoDRGNおよびその背景となる深層学習のトピックについて解説を行ったのち，構造分類のベンチマークとして6種類の複合体を有するGroEL/ESの実データについて三次元再構成とその分類を試みた．","subitem_description_language":"ja","subitem_description_type":"Abstract"},{"subitem_description":"Cryo-EM single particle analysis can solve multiple protein structures contained in a sample by classification. However, the information on the dynamics between the solved structures is lost and it can only be inferred. About this problem, cryoDRGN, a deep learning approach for solving the three-dimensional reconstruction and structural classification that was announced in 2020, breaks away from discrete data partition. The method is based on an auto-encoder, and realizes continuous structural classification by constructing a latent space that separates the information depending on the projection parameters from the input particle image. In this paper, we explain conventional classification method in single particle analysis and deep learning topics that are the background of cryoDRGN. Then, as a benchmark for structural classification, we try three-dimensional reconstruction on the actual data of GroEL/ES having 6 kinds of complexes.","subitem_description_language":"en","subitem_description_type":"Abstract"}]},"item_21_description_60":{"attribute_name":"資源タイプ","attribute_value_mlt":[{"subitem_description":"Conference Paper","subitem_description_type":"Other"}]},"item_21_full_name_3":{"attribute_name":"著者別名","attribute_value_mlt":[{"nameIdentifiers":[{"nameIdentifier":"32447","nameIdentifierScheme":"WEKO"}],"names":[{"name":"Mamizu,  Nobuya"}]},{"nameIdentifiers":[{"nameIdentifier":"32448","nameIdentifierScheme":"WEKO"}],"names":[{"name":"Tanaka,  Kotaro"}]},{"affiliations":[{"affiliationNames":[{"affiliationName":"","lang":"ja"}],"nameIdentifiers":[]}],"familyNames":[{"familyName":"Yasunaga","familyNameLang":"en"},{"familyName":"安永","familyNameLang":"ja"},{"familyName":"ヤスナガ","familyNameLang":"ja-Kana"}],"givenNames":[{"givenName":"Takuo","givenNameLang":"en"},{"givenName":"卓生","givenNameLang":"ja"},{"givenName":"タクオ","givenNameLang":"ja-Kana"}],"nameIdentifiers":[{"nameIdentifier":"7216","nameIdentifierScheme":"WEKO"},{"nameIdentifier":"60251394","nameIdentifierScheme":"e-Rad","nameIdentifierURI":"https://nrid.nii.ac.jp/ja/nrid/1000060251394"},{"nameIdentifier":"7006317534","nameIdentifierScheme":"Scopus著者ID","nameIdentifierURI":"https://www.scopus.com/authid/detail.uri?authorId=7006317534"},{"nameIdentifier":"0000-0002-2494-9603","nameIdentifierScheme":"ORCiD","nameIdentifierURI":"https://orcid.org/0000-0002-2494-9603"},{"nameIdentifier":"266","nameIdentifierScheme":"九工大研究者情報","nameIdentifierURI":"https://hyokadb02.jimu.kyutech.ac.jp/html/266_ja.html"},{"nameIdentifier":"7216","nameIdentifierScheme":"Article by this author","nameIdentifierURI":"https://kyutech.repo.nii.ac.jp/search?page=1&size=20&sort=wtl&search_type=0&q=&wid=7216"}],"names":[{"name":"Yasunaga, Takuo","nameLang":"en"},{"name":"安永, 卓生","nameLang":"ja"},{"name":"ヤスナガ, タクオ","nameLang":"ja-Kana"}]}]},"item_21_link_62":{"attribute_name":"研究者情報","attribute_value_mlt":[{"subitem_link_url":"https://hyokadb02.jimu.kyutech.ac.jp/html/266_ja.html"}]},"item_21_publisher_7":{"attribute_name":"出版者","attribute_value_mlt":[{"subitem_publisher":"日本顕微鏡学会","subitem_publisher_language":"ja"}]},"item_21_relation_12":{"attribute_name":"DOI","attribute_value_mlt":[{"subitem_relation_type":"isIdenticalTo","subitem_relation_type_id":{"subitem_relation_type_id_text":"https://doi.org/10.11410/kenbikyo.55.3_104","subitem_relation_type_select":"DOI"}}]},"item_21_relation_66":{"attribute_name":"論文ID（NAID）","attribute_value_mlt":[{"subitem_relation_type":"isIdenticalTo","subitem_relation_type_id":{"subitem_relation_type_id_text":"130007967723","subitem_relation_type_select":"NAID"}}]},"item_21_rights_13":{"attribute_name":"権利","attribute_value_mlt":[{"subitem_rights":"Copyright (c) 2020 The Japanese Society of Microscopy"}]},"item_21_select_59":{"attribute_name":"査読の有無","attribute_value_mlt":[{"subitem_select_item":"yes"}]},"item_21_source_id_10":{"attribute_name":"書誌レコードID","attribute_value_mlt":[{"subitem_source_identifier":"AA11917781","subitem_source_identifier_type":"NCID"}]},"item_21_source_id_8":{"attribute_name":"ISSN","attribute_value_mlt":[{"subitem_source_identifier":"2434-2386","subitem_source_identifier_type":"EISSN"},{"subitem_source_identifier":"1349-0958","subitem_source_identifier_type":"PISSN"}]},"item_21_subject_16":{"attribute_name":"日本十進分類法","attribute_value_mlt":[{"subitem_subject":"549","subitem_subject_scheme":"NDC"}]},"item_21_text_28":{"attribute_name":"論文ID（連携）","attribute_value_mlt":[{"subitem_text_value":"10361542"}]},"item_21_text_63":{"attribute_name":"連携ID","attribute_value_mlt":[{"subitem_text_value":"9572"}]},"item_21_version_type_58":{"attribute_name":"著者版フラグ","attribute_value_mlt":[{"subitem_version_resource":"http://purl.org/coar/version/c_970fb48d4fbd8a85","subitem_version_type":"VoR"}]},"item_creator":{"attribute_name":"著者","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"馬水,  信弥","creatorNameLang":"ja"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"田中,  康太郎","creatorNameLang":"ja"}],"nameIdentifiers":[{}]},{"creatorAffiliations":[{"affiliationNameIdentifiers":[],"affiliationNames":[{"affiliationName":""}]}],"creatorNames":[{"creatorName":"Yasunaga, Takuo","creatorNameLang":"en"},{"creatorName":"安永, 卓生","creatorNameLang":"ja"},{"creatorName":"ヤスナガ, タクオ","creatorNameLang":"ja-Kana"}],"familyNames":[{},{},{}],"givenNames":[{},{},{}],"nameIdentifiers":[{},{},{},{},{},{}]}]},"item_files":{"attribute_name":"ファイル情報","attribute_type":"file","attribute_value_mlt":[{"accessrole":"open_date","date":[{"dateType":"Available","dateValue":"2022-02-02"}],"displaytype":"detail","filename":"kenbikyo.55.3_104.pdf","filesize":[{"value":"2.0 MB"}],"format":"application/pdf","licensetype":"license_note","mimetype":"application/pdf","url":{"label":"kenbikyo.55.3_104.pdf","url":"https://kyutech.repo.nii.ac.jp/record/7503/files/kenbikyo.55.3_104.pdf"},"version_id":"ec5bd368-410c-4b5c-a484-c1296ece8a11"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"単粒子解析","subitem_subject_language":"ja","subitem_subject_scheme":"Other"},{"subitem_subject":"クラス分類","subitem_subject_language":"ja","subitem_subject_scheme":"Other"},{"subitem_subject":"深層学習","subitem_subject_language":"ja","subitem_subject_scheme":"Other"},{"subitem_subject":"オートエンコーダー","subitem_subject_language":"ja","subitem_subject_scheme":"Other"},{"subitem_subject":"single particle analysis","subitem_subject_language":"en","subitem_subject_scheme":"Other"},{"subitem_subject":"structural classification of proteins","subitem_subject_language":"en","subitem_subject_scheme":"Other"},{"subitem_subject":"deep learning","subitem_subject_language":"en","subitem_subject_scheme":"Other"},{"subitem_subject":"auto-encorder","subitem_subject_language":"en","subitem_subject_scheme":"Other"}]},"item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"jpn"}]},"item_resource_type":{"attribute_name":"資源タイプ","attribute_value_mlt":[{"resourcetype":"journal article","resourceuri":"http://purl.org/coar/resource_type/c_6501"}]},"item_title":"単粒子解析におけるタンパク質構造分類のための深層学習アプローチの動向","item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"単粒子解析におけるタンパク質構造分類のための深層学習アプローチの動向","subitem_title_language":"ja"},{"subitem_title":"Trends in Deep Learning Approaches for Protein Structure Classification in Single Particle Analysis","subitem_title_language":"en"}]},"item_type_id":"21","owner":"3","path":["24"],"pubdate":{"attribute_name":"PubDate","attribute_value":"2022-02-02"},"publish_date":"2022-02-02","publish_status":"0","recid":"7503","relation_version_is_last":true,"title":["単粒子解析におけるタンパク質構造分類のための深層学習アプローチの動向"],"weko_creator_id":"3","weko_shared_id":-1},"updated":"2023-10-25T09:18:13.737104+00:00"}