{"_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": ["1642838338003", "1642838406845"]}, "author_link": [], "item_1617186331708": {"attribute_name": "Title", "attribute_value_mlt": [{"subitem_1551255647225": "PCA\u2011based unsupervised feature extraction for gene expression analysis of COVID\u201119 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.\u2011H.", "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": "\u00a9 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-\u03baB) 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-\u03baB activity was suppressed by H3K36me3 in COVID-19 patient blood.", "subitem_description_type": "Other"}, {"subitem_description": "\u8ad6\u6587", "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": [{"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", "download_preview_message": "", "file_order": 0, "filename": "s41598-021-95698-w.pdf", "future_date_message": "", "is_thumbnail": false, "mimetype": "", "size": 0, "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\u2011based unsupervised feature extraction for gene expression analysis of COVID\u201119 patients", "item_type_id": "15", "owner": "1", "path": ["1642838338003", "1642838406845"], "permalink_uri": "http://hdl.handle.net/20.500.12000/49789", "pubdate": {"attribute_name": "PubDate", "attribute_value": "2021-09-30"}, "publish_date": "2021-09-30", "publish_status": "0", "recid": "2011748", "relation": {}, "relation_version_is_last": true, "title": ["PCA\u2011based unsupervised feature extraction for gene expression analysis of COVID\u201119 patients"], "weko_shared_id": -1}