{"metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00220086","sets":["1164:5352:10882:11013"]},"path":["11013"],"owner":"44499","recid":"220086","title":["Network-based pathogenicity prediction for genomic variants"],"pubdate":{"attribute_name":"公開日","attribute_value":"2022-09-05"},"_buckets":{"deposit":"c93e3707-a483-476a-97ac-6371433a0b2f"},"_deposit":{"id":"220086","pid":{"type":"depid","value":"220086","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"Network-based pathogenicity prediction for genomic variants","author_link":["574979","574977","574980","574978","574976","574975","574981","574974"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"Network-based pathogenicity prediction for genomic variants"},{"subitem_title":"Network-based pathogenicity prediction for genomic variants","subitem_title_language":"en"}]},"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":"Kyoto University"},{"subitem_text_value":"Kyoto University"},{"subitem_text_value":"Kyoto University"},{"subitem_text_value":"Kyoto University"}]},"item_4_text_4":{"attribute_name":"著者所属(英)","attribute_value_mlt":[{"subitem_text_value":"Kyoto University","subitem_text_language":"en"},{"subitem_text_value":"Kyoto University","subitem_text_language":"en"},{"subitem_text_value":"Kyoto University","subitem_text_language":"en"},{"subitem_text_value":"Kyoto University","subitem_text_language":"en"}]},"item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"eng"}]},"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/220086/files/IPSJ-BIO22071003.pdf","label":"IPSJ-BIO22071003.pdf"},"date":[{"dateType":"Available","dateValue":"2024-09-05"}],"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-BIO22071003.pdf","filesize":[{"value":"370.2 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":"200db17c-4a2d-493e-8402-98a2fe8e1ce6","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":"Mayumi, Kamada"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Atsuko, Takagi"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Ryosuke, Kojima"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Yasushi, Okuno"}],"nameIdentifiers":[{}]}]},"item_4_creator_6":{"attribute_name":"著者名(英)","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Mayumi, Kamada","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Atsuko, Takagi","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Ryosuke, Kojima","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Yasushi, Okuno","creatorNameLang":"en"}],"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":"Genomic medicine is expected to play a key role in realizing precision medicine. On the other hand, only a few percent of the huge number of genomic variants detected by genome analysis have clear clinical significance. Many computational methods have been developed to predict the pathogenicity of variants. Although the mechanisms of diseases involve complex biomolecular interactions, conventional methods have not been able to consider the molecular relationship. In this study, we developed PathoGN, which represents the molecular network as graphs and predicts the pathogenicity of variants using Graph Convolutional Networks. In performance evaluation using benchmark sets, PathoGN outperformed existing methods. Results of evaluation with ClinVar, a database of disease-related variants, PathoGN was shown to accurately predict the current label of a variant whose significance was unknown in the past. These results suggest the usefulness of considering biological networks.","subitem_description_type":"Other"}]},"item_4_description_8":{"attribute_name":"論文抄録(英)","attribute_value_mlt":[{"subitem_description":"Genomic medicine is expected to play a key role in realizing precision medicine. On the other hand, only a few percent of the huge number of genomic variants detected by genome analysis have clear clinical significance. Many computational methods have been developed to predict the pathogenicity of variants. Although the mechanisms of diseases involve complex biomolecular interactions, conventional methods have not been able to consider the molecular relationship. In this study, we developed PathoGN, which represents the molecular network as graphs and predicts the pathogenicity of variants using Graph Convolutional Networks. In performance evaluation using benchmark sets, PathoGN outperformed existing methods. Results of evaluation with ClinVar, a database of disease-related variants, PathoGN was shown to accurately predict the current label of a variant whose significance was unknown in the past. These results suggest the usefulness of considering biological networks.","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":"3","bibliographicVolumeNumber":"2022-BIO-71"}]},"relation_version_is_last":true,"weko_creator_id":"44499"},"id":220086,"updated":"2025-01-19T14:40:24.343858+00:00","links":{},"created":"2025-01-19T01:20:08.141134+00:00"}