{"created":"2022-02-01T06:55:37.294252+00:00","id":2011748,"links":{},"metadata":{"_buckets":{"deposit":"0f60447f-5516-40b1-bcc2-e13e39b0905f"},"_deposit":{"id":"2011748","owners":[1],"pid":{"revision_id":0,"type":"depid","value":"2011748"},"status":"published"},"_oai":{"id":"oai:u-ryukyu.repo.nii.ac.jp:02011748","sets":["1642838163960:1642838338003","1642838403551:1642838406845"]},"author_link":[],"item_1617186331708":{"attribute_name":"Title","attribute_value_mlt":[{"subitem_1551255647225":"PCA‑based unsupervised feature extraction for gene expression analysis of COVID‑19 patients","subitem_1551255648112":"ja"}]},"item_1617186419668":{"attribute_name":"Creator","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Fujisawa, Kota","creatorNameLang":"en"}]},{"creatorNames":[{"creatorName":"Shimo, Mamoru","creatorNameLang":"en"}]},{"creatorNames":[{"creatorName":"Taguchi, Y.‑H.","creatorNameLang":"ja"}]},{"creatorNames":[{"creatorName":"Ikematsu, Shinya","creatorNameLang":"en"}]},{"creatorNames":[{"creatorName":"Miyata, Ryota","creatorNameLang":"en"}]}]},"item_1617186476635":{"attribute_name":"Access Rights","attribute_value_mlt":[{"subitem_1522299639480":"open access","subitem_1600958577026":"http://purl.org/coar/access_right/c_abf2"}]},"item_1617186499011":{"attribute_name":"Rights","attribute_value_mlt":[{"subitem_1522650717957":"ja","subitem_1522651041219":"© The Author(s) 2021"},{"subitem_1522650717957":"en","subitem_1522651041219":"Creative Commons Attribution 4.0"},{"subitem_1522650717957":"en","subitem_1522650727486":"https://creativecommons.org/licenses/by/4.0/","subitem_1522651041219":"https://creativecommons.org/licenses/by/4.0/"}]},"item_1617186626617":{"attribute_name":"Description","attribute_value_mlt":[{"subitem_description":"Coronavirus disease 2019 (COVID-19) is raging worldwide. This potentially fatal infectious disease is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). However, the complete mechanism of COVID-19 is not well understood. Therefore, we analyzed gene expression profiles of COVID-19 patients to identify disease-related genes through an innovative machine learning method that enables a data-driven strategy for gene selection from a data set with a small number of samples and many candidates. Principal-component-analysis-based unsupervised feature extraction (PCAUFE) was applied to the RNA expression profiles of 16 COVID-19 patients and 18 healthy control subjects. The results identified 123 genes as critical for COVID-19 progression from 60,683 candidate probes, including immune-related genes. The 123 genes were enriched in binding sites for transcription factors NFKB1 and RELA, which are involved in various biological phenomena such as immune response and cell survival: the primary mediator of canonical nuclear factor-kappa B (NF-κB) activity is the heterodimer RelA-p50. The genes were also enriched in histone modification H3K36me3, and they largely overlapped the target genes of NFKB1 and RELA. We found that the overlapping genes were downregulated in COVID-19 patients. These results suggest that canonical NF-κB activity was suppressed by H3K36me3 in COVID-19 patient blood.","subitem_description_type":"Other"},{"subitem_description":"論文","subitem_description_type":"Other"}]},"item_1617186643794":{"attribute_name":"Publisher","attribute_value_mlt":[{"subitem_1522300295150":"en","subitem_1522300316516":"Nature Research"}]},"item_1617186702042":{"attribute_name":"Language","attribute_value_mlt":[{"subitem_1551255818386":"eng"}]},"item_1617186783814":{"attribute_name":"Identifier","attribute_value_mlt":[{"subitem_identifier_type":"HDL","subitem_identifier_uri":"http://hdl.handle.net/20.500.12000/49789"}]},"item_1617186920753":{"attribute_name":"Source Identifier","attribute_value_mlt":[{"subitem_1522646500366":"ISSN","subitem_1522646572813":"2045-2322"}]},"item_1617186941041":{"attribute_name":"Source Title","attribute_value_mlt":[{"subitem_1522650068558":"en","subitem_1522650091861":"Scientific Reports"}]},"item_1617187056579":{"attribute_name":"Bibliographic Information","attribute_value_mlt":[{"bibliographicIssueDates":{"bibliographicIssueDate":"2021-08-30","bibliographicIssueDateType":"Issued"},"bibliographicVolumeNumber":"11"}]},"item_1617258105262":{"attribute_name":"Resource Type","attribute_value_mlt":[{"resourcetype":"journal article","resourceuri":"http://purl.org/coar/resource_type/c_6501"}]},"item_1617265215918":{"attribute_name":"Version Type","attribute_value_mlt":[{"subitem_1522305645492":"VoR","subitem_1600292170262":"http://purl.org/coar/version/c_970fb48d4fbd8a85"}]},"item_1617353299429":{"attribute_name":"Relation","attribute_value_mlt":[{"subitem_1522306287251":{"subitem_1522306382014":"DOI","subitem_1522306436033":"https://doi.org/10.1038/s41598-021-95698-w"}},{"subitem_1522306287251":{"subitem_1522306382014":"DOI","subitem_1522306436033":"https://doi.org/10.1038/s41598-021-95698-w"}}]},"item_1617605131499":{"attribute_name":"File","attribute_type":"file","attribute_value_mlt":[{"accessrole":"open_access","filename":"s41598-021-95698-w.pdf","mimetype":"application/pdf","url":{"objectType":"fulltext","url":"https://u-ryukyu.repo.nii.ac.jp/record/2011748/files/s41598-021-95698-w.pdf"},"version_id":"4f3ffba7-6473-4f61-a328-bd06de1ac3b0"}]},"item_title":"PCA‑based unsupervised feature extraction for gene expression analysis of COVID‑19 patients","item_type_id":"15","owner":"1","path":["1642838338003","1642838406845"],"pubdate":{"attribute_name":"PubDate","attribute_value":"2021-09-30"},"publish_date":"2021-09-30","publish_status":"0","recid":"2011748","relation_version_is_last":true,"title":["PCA‑based unsupervised feature extraction for gene expression analysis of COVID‑19 patients"],"weko_creator_id":"1","weko_shared_id":-1},"updated":"2023-08-03T05:29:15.206131+00:00"}