{"created":"2025-01-19T01:00:00.777460+00:00","metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00194993","sets":["1164:5352:9740:9741"]},"path":["9741"],"owner":"44499","recid":"194993","title":["主成分分析を用いた教師無し学習による変数選択の一細胞RNA-seqへの応用"],"pubdate":{"attribute_name":"公開日","attribute_value":"2019-03-01"},"_buckets":{"deposit":"d6e36f0e-7c38-48d7-8672-afc791584d18"},"_deposit":{"id":"194993","pid":{"type":"depid","value":"194993","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"主成分分析を用いた教師無し学習による変数選択の一細胞RNA-seqへの応用","author_link":["463100"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"主成分分析を用いた教師無し学習による変数選択の一細胞RNA-seqへの応用"}]},"item_type_id":"4","publish_date":"2019-03-01","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"}]},"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/194993/files/IPSJ-BIO19057006.pdf","label":"IPSJ-BIO19057006.pdf"},"date":[{"dateType":"Available","dateValue":"2021-03-01"}],"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-BIO19057006.pdf","filesize":[{"value":"1.8 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":"a11aaa76-51c4-4d93-8a00-049aef61bf13","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2019 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"}],"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":"一細胞RNA-seqは従来の臓器レベルの平均化された遺伝子発現プロファイルの観測を超えて、細胞ごとの発現プロファイルを観測できるという意味で画期的である。一方、個々の細胞にはラベルがついていないため、従来の臓器レベルの観測の様に、正常臓器と腫瘍で差が大きい遺伝子を選ぶ、などの基準で遺伝子を選択することができない。遺伝子を選択することはtSNEなどのクラスタリングによる可視化を行う場合にも非常に重要なプロセスである。このため、ラベルを用いない教師なし学習による変数選択の方法がいくつか提案されてきた。ここでは、著者が従来から提唱している「主成分分析を用いた教師なし学習による変数選択法」を一細胞RNA-seqにおける遺伝子選択に用いた場合を考察し、他の手法(highly variable genes，bimodal genes，dpFeature)による変数選択との比較を行う。","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":"2019-03-01","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"6","bibliographicVolumeNumber":"2019-BIO-57"}]},"relation_version_is_last":true,"weko_creator_id":"44499"},"id":194993,"updated":"2025-01-19T23:15:29.784162+00:00","links":{}}