Detecting time-evolving phenotypic components of adverse reactions against BNT162b2 SARS-CoV-2 vaccine via non-negative tensor factorization

Detecting time-evolving phenotypic components of adverse reactions against BNT162b2 SARS-CoV-2 vaccine via non-negative tensor factorization

Summary: Symptoms of adverse reactions to vaccines evolve over time, but traditional studies have focused only on the frequency and intensity of symptoms. Here, we attempt to extract the dynamic changes in vaccine adverse reaction symptoms as a small number of interpretable components by using non-n...

Full description

Bibliographic Details
Main Authors: Kei Ikeda, Taka-Aki Nakada, Takahiro Kageyama, Shigeru Tanaka, Naoki Yoshida, Tetsuo Ishikawa, Yuki Goshima, Natsuko Otaki, Shingo Iwami, Teppei Shimamura, Toshibumi Taniguchi, Hidetoshi Igari, Hideki Hanaoka, Koutaro Yokote, Koki Tsuyuzaki, Hiroshi Nakajima, Eiryo Kawakami
Format: Article
Language:English
Published: Elsevier 2022-10-01
Series:iScience
Subjects:
Immunology
computational bioinformatics
machine learning
Online Access:http://www.sciencedirect.com/science/article/pii/S2589004222015097

Internet

http://www.sciencedirect.com/science/article/pii/S2589004222015097

Similar Items