{"created":"2025-01-18T23:21:45.797120+00:00","updated":"2025-01-22T03:50:31.728606+00:00","metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00058874","sets":["1164:5352:5353:5355"]},"path":["5355"],"owner":"1","recid":"58874","title":["超高次元時系列データからの遺伝子ネットワーク推定について"],"pubdate":{"attribute_name":"公開日","attribute_value":"2008-09-11"},"_buckets":{"deposit":"7ba2988e-079c-46b6-b1da-ee21f98004f5"},"_deposit":{"id":"58874","pid":{"type":"depid","value":"58874","revision_id":0},"owners":[1],"status":"published","created_by":1},"item_title":"超高次元時系列データからの遺伝子ネットワーク推定について","author_link":["0","0"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"超高次元時系列データからの遺伝子ネットワーク推定について"},{"subitem_title":"Estimating Large-Scale Gene Networks from Ultra High-Dimensional Time-Series Data","subitem_title_language":"en"}]},"item_type_id":"4","publish_date":"2008-09-11","item_4_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"東京大学医科学研究所ヒトゲノム解析センター"},{"subitem_text_value":"東京大学医科学研究所ヒトゲノム解析センター"},{"subitem_text_value":"東京大学医科学研究所ヒトゲノム解析センター"}]},"item_4_text_4":{"attribute_name":"著者所属(英)","attribute_value_mlt":[{"subitem_text_value":"Human Genome Center, Institute of Medical Science, University of Tokyo","subitem_text_language":"en"},{"subitem_text_value":"Human Genome Center, Institute of Medical Science, University of Tokyo","subitem_text_language":"en"},{"subitem_text_value":"Human Genome Center, Institute of Medical Science, University of Tokyo","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/58874/files/IPSJ-BIO08014010.pdf"},"date":[{"dateType":"Available","dateValue":"2010-09-11"}],"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-BIO08014010.pdf","filesize":[{"value":"1.6 MB"}],"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":"8a9da716-a913-4d1e-8152-d51623632f65","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2008 by the Information Processing Society of Japan"}]},"item_4_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"島村, 徹平"},{"creatorName":"井元, 清哉"},{"creatorName":"宮野, 悟"}],"nameIdentifiers":[{}]}]},"item_4_creator_6":{"attribute_name":"著者名(英)","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Teppei, Shimamura","creatorNameLang":"en"},{"creatorName":"Seiya, Imoto","creatorNameLang":"en"},{"creatorName":"Satoru, Miyano","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_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"本報告では、時系列に観測されるマイクロアレイデータからベクトル自己回帰モデルに基づいて遺伝子ネットワークを推定する問題を考える。ベクトル自己回帰モデルは、多変量時系列における変数間の依存関係を明示的に記述したモデルであり、モデルの係数行列により、Granger　によって定義される因果関係が与えられる。実際の問題においては、超高次元時系列データから係数行列の推定、特に係数行列のどの要素が 0 になるかを推定することが本質的な問題である。以上の問題に対し、係数行列のパラメータを再帰的に推定する新しい正則化法を提案し、それにより遺伝子ネットワークを推定する手法について述べる。","subitem_description_type":"Other"}]},"item_4_description_8":{"attribute_name":"論文抄録(英)","attribute_value_mlt":[{"subitem_description":"The vector autoregressive model has been considered as a promising tool to reconstruct large-scale gene networks from time course microarray data. However, it remains a challenging problem due to the small sample size and the high-dimensionality of time course microarray data. We present a novel regression-based modeling strategy with a new class of regularization for estimating gene networks.","subitem_description_type":"Other"}]},"item_4_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"42","bibliographic_titles":[{"bibliographic_title":"情報処理学会研究報告バイオ情報学（BIO）"}],"bibliographicPageStart":"39","bibliographicIssueDates":{"bibliographicIssueDate":"2008-09-11","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"86(2008-BIO-014)","bibliographicVolumeNumber":"2008"}]},"relation_version_is_last":true,"weko_creator_id":"1"},"id":58874,"links":{}}