{"id":236955,"updated":"2025-01-19T09:03:23.171549+00:00","links":{},"created":"2025-01-19T01:39:18.377100+00:00","metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00236955","sets":["6504:11678:11684"]},"path":["11684"],"owner":"44499","recid":"236955","title":["機械学習を用いたゲノム多型データによる日本人の出身都道府県の予測"],"pubdate":{"attribute_name":"公開日","attribute_value":"2024-03-01"},"_buckets":{"deposit":"f0ba684e-e427-4401-b302-8fc7b1dee130"},"_deposit":{"id":"236955","pid":{"type":"depid","value":"236955","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"機械学習を用いたゲノム多型データによる日本人の出身都道府県の予測","author_link":["647885","647888","647887","647886"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"機械学習を用いたゲノム多型データによる日本人の出身都道府県の予測"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"コンピュータと人間社会","subitem_subject_scheme":"Other"}]},"item_type_id":"22","publish_date":"2024-03-01","item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"jpn"}]},"item_22_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"東海大"},{"subitem_text_value":"ジーンクエスト"},{"subitem_text_value":"ジーンクエスト"},{"subitem_text_value":"東海大"}]},"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/236955/files/IPSJ-Z86-5ZJ-06.pdf","label":"IPSJ-Z86-5ZJ-06.pdf"},"date":[{"dateType":"Available","dateValue":"2024-07-04"}],"format":"application/pdf","filename":"IPSJ-Z86-5ZJ-06.pdf","filesize":[{"value":"551.5 kB"}],"mimetype":"application/pdf","accessrole":"open_date","version_id":"8d0a0c10-e961-4d07-8e38-669e9500a0c2","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2024 by the Information Processing Society of Japan"}]},"item_22_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"松永, 一真"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"野川, 駿"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"齋藤, 憲司"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"今西, 規"}],"nameIdentifiers":[{}]}]},"item_resource_type":{"attribute_name":"資源タイプ","attribute_value_mlt":[{"resourceuri":"http://purl.org/coar/resource_type/c_5794","resourcetype":"conference paper"}]},"item_22_source_id_9":{"attribute_name":"書誌レコードID","attribute_value_mlt":[{"subitem_source_identifier":"AN00349328","subitem_source_identifier_type":"NCID"}]},"item_22_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"われわれは機械学習を用いて、ゲノム多型データによる日本人の出身都道府県の予測を行った。SNPアレイを用いてタイピングされた約21万6千ヶ所のSNPsデータの対立遺伝子頻度から、各都道府県のジェノタイプ頻度をそれぞれ計算した。ジェノタイプ頻度を基に各都道府県の遺伝子型シミュレーションデータを作成し、これと実際の日本人のゲノム多型データを機械学習にかけた。Pythonの機械学習ライブラリであるscikit-learnを用いて複数のモデルで検証し、出身都道府県の予測精度について考察した。その結果、以前に作成したベイズ推定よりも高い正解率を達成することができた。","subitem_description_type":"Other"}]},"item_22_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"692","bibliographic_titles":[{"bibliographic_title":"第86回全国大会講演論文集"}],"bibliographicPageStart":"691","bibliographicIssueDates":{"bibliographicIssueDate":"2024-03-01","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"1","bibliographicVolumeNumber":"2024"}]},"relation_version_is_last":true,"weko_creator_id":"44499"}}