PCA-based unsupervised feature extraction for gene expression analysis of COVID-19 patients

• Published in: Scientific reports Vol. 11; no. 1; pp. 17351 - 11
• Main Authors: Fujisawa, Kota, Shimo, Mamoru, Taguchi, Y.-H., Ikematsu, Shinya, Miyata, Ryota
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 30.08.2021; Nature Publishing Group; Nature Portfolio
• Subjects: 631/114/1305; 639/705/1042; Binding sites; Cell survival; Coronaviruses; COVID-19; DNA probes; Gene expression; Histones; Humanities and Social Sciences; Immune response; Infectious diseases; Learning algorithms; Machine learning; multidisciplinary; NF-κB protein; RelA protein; Science; Science (multidisciplinary); Severe acute respiratory syndrome coronavirus 2; Transcription factors
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-021-95698-w

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-1...