{"created":"2025-01-19T01:20:08.083315+00:00","updated":"2025-01-19T14:40:26.022251+00:00","metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00220085","sets":["1164:5352:10882:11013"]},"path":["11013"],"owner":"44499","recid":"220085","title":["遺伝子発現量と知識グラフを組み合わせた深層学習モデルのよるがん患者予後予測"],"pubdate":{"attribute_name":"公開日","attribute_value":"2022-09-05"},"_buckets":{"deposit":"171ff029-38bf-425f-9115-7a935f186a17"},"_deposit":{"id":"220085","pid":{"type":"depid","value":"220085","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"遺伝子発現量と知識グラフを組み合わせた深層学習モデルのよるがん患者予後予測","author_link":["574970","574973","574971","574972"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"遺伝子発現量と知識グラフを組み合わせた深層学習モデルのよるがん患者予後予測"}]},"item_type_id":"4","publish_date":"2022-09-05","item_4_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"京都大学大学院医学研究科人間健康科学系専攻"},{"subitem_text_value":"京都大学大学院医学研究科人間健康科学系専攻"},{"subitem_text_value":"京都大学大学院医学研究科人間健康科学系専攻"},{"subitem_text_value":"京都大学大学院医学研究科人間健康科学系専攻"}]},"item_4_text_4":{"attribute_name":"著者所属(英)","attribute_value_mlt":[{"subitem_text_value":"Human Health Sciences, Graduate School of Medicine, Kyoto University","subitem_text_language":"en"},{"subitem_text_value":"Human Health Sciences, Graduate School of Medicine, Kyoto University","subitem_text_language":"en"},{"subitem_text_value":"Human Health Sciences, Graduate School of Medicine, Kyoto University","subitem_text_language":"en"},{"subitem_text_value":"Human Health Sciences, Graduate School of Medicine, Kyoto University","subitem_text_language":"en"}]},"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/220085/files/IPSJ-BIO22071002.pdf","label":"IPSJ-BIO22071002.pdf"},"date":[{"dateType":"Available","dateValue":"2024-09-05"}],"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-BIO22071002.pdf","filesize":[{"value":"568.0 kB"}],"mimetype":"application/pdf","priceinfo":[{"tax":["include_tax"],"price":"660","billingrole":"5"},{"tax":["include_tax"],"price":"330","billingrole":"6"},{"tax":["include_tax"],"price":"0","billingrole":"41"},{"tax":["include_tax"],"price":"0","billingrole":"44"}],"accessrole":"open_date","version_id":"7d13913a-c4ed-45b3-949d-3ae84401a040","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2022 by the Information Processing Society of Japan"}]},"item_4_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"井上, 和真"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"小島, 諒介"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"鎌田, 真由美"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"奥野, 恭史"}],"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":"医療技術と AI 技術の進歩は Precision medicine 実現に向け，重要な位置に占めている．今回我々は，がん患者の遺伝子発現量情報と知識グラフをベースとした分子間相互作用情報を組み合わせて生存予測を行う新規深層学習手法を開発した．検証の結果，我々の提案手法は，遺伝子発現量情報のみを用いる従来手法よりも高精度に予測ができることを示した．また患者の臨床情報としてがん種情報を加えることで，さらに高精度に生存予測を行うことができた．","subitem_description_type":"Other"}]},"item_4_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"2","bibliographic_titles":[{"bibliographic_title":"研究報告バイオ情報学（BIO）"}],"bibliographicPageStart":"1","bibliographicIssueDates":{"bibliographicIssueDate":"2022-09-05","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"2","bibliographicVolumeNumber":"2022-BIO-71"}]},"relation_version_is_last":true,"weko_creator_id":"44499"},"id":220085,"links":{}}