{"links":{},"id":2008108,"metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:02008108","sets":["1164:5352:1772177578196:1772177778740"]},"path":["1772177778740"],"owner":"80578","recid":"2008108","title":["適応的テンソル分解およびPCAに基づく教師なし特徴抽出法は従来法よりも生物学的に妥当な発現変動遺伝子を選択する"],"pubdate":{"attribute_name":"PubDate","attribute_value":"2026-03-05"},"_buckets":{"deposit":"9e91c79e-9906-40bb-8acd-6e7415b6a6f9"},"_deposit":{"id":"2008108","pid":{"type":"depid","value":"2008108","revision_id":0},"owners":[80578],"status":"published","created_by":80578},"item_title":"適応的テンソル分解およびPCAに基づく教師なし特徴抽出法は従来法よりも生物学的に妥当な発現変動遺伝子を選択する","author_link":[],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"適応的テンソル分解およびPCAに基づく教師なし特徴抽出法は従来法よりも生物学的に妥当な発現変動遺伝子を選択する","subitem_title_language":"ja"}]},"item_type_id":"4","publish_date":"2026-03-05","item_4_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"中央大学理工学部物理学科"},{"subitem_text_value":"キング・アブドゥルアズィーズ大学コンピュータサイエンス学科"}]},"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/2008108/files/IPSJ-BIO26084008.pdf","label":"IPSJ-BIO26084008.pdf"},"date":[{"dateType":"Available","dateValue":"2028-03-05"}],"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-BIO26084008.pdf","filesize":[{"value":"1.2 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":"c117ff5e-b701-41a2-a597-8ba53026a635","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2026 by the Information Processing Society of Japan"}]},"item_4_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"田口,善弘"}]},{"creatorNames":[{"creatorName":"Turki,Turki"}]}]},"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":"本稿では、我々が提案する主成分分析(PCA)およびテンソル分解(TD)に碁づく教師なし特徴抽出(FE)法の改良版について、その詳細なアルゴリズムと生物学的有効性を解説する。これまで我々が開発してきたTD/PCAに碁づくFE法は、薬剤転用やバイオマーカー探索など広範なゲノム解析に応用され、その有用性が示されてきた。しかし、従来の手法には、選択される遺伝子数が保守的すぎ(過少であり)、偽陰性の懸念がある点や、導出されるP値の分布が理論的な帰無仮説(ガウス分布)と完全に一致しないという統計的な課題が残されていた。そこで本研究では、P値のヒストグラムが帰無仮説に可能な限り一致するように、特異値ベクトルの標準偏差をデータ駆動的に最適化する新たなアルゴリズムを導入した。これにより、理論的な仮定と実データの乖離を補正し、統計的に妥当な遺伝子選択が可能となる。実際のRNA-seqデータ(腎淡明細胞癌、マウス肝臓モデル等)を用いた検証の結果、本手法はDESeq2やedgeRといった負の二項分布や分散関係を経験的に仮定する必要がある最先端の従来法と比較して、より多くの、かつ生物学的により合理的(Gene Ontologyタームの濃縮などにより評価)な発現変動遺伝子(DEG)を選択できることを示した。本手法は、特にサンプル数が少なく次元数が高い(Small-N, Large-P)問題において、強力なツールとなる。","subitem_description_type":"Other"}]},"item_4_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"6","bibliographic_titles":[{"bibliographic_title":"研究報告バイオ情報学（BIO）"}],"bibliographicPageStart":"1","bibliographicIssueDates":{"bibliographicIssueDate":"2026-03-05","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"8","bibliographicVolumeNumber":"2026-BIO-84"}]},"relation_version_is_last":true,"weko_creator_id":"80578"},"created":"2026-02-27T07:47:06.957633+00:00","updated":"2026-02-27T07:47:11.209785+00:00"}