2021
DOI: 10.1016/j.csbj.2021.05.036
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A computational workflow for the detection of candidate diagnostic biomarkers of Kawasaki disease using time-series gene expression data

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Cited by 9 publications
(4 citation statements)
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References 44 publications
(38 reference statements)
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“…As expected based on past machine learning applications on gene expression data [45] , juxtaposition of the models’ performance showed relatively small differences. In brief, evaluation on the testing set placed our lite model among the top performing methods, while Xu-based classifier showed the lowest efficiency in separating IPF from control individuals.…”
Section: Discussionsupporting
confidence: 76%
“…As expected based on past machine learning applications on gene expression data [45] , juxtaposition of the models’ performance showed relatively small differences. In brief, evaluation on the testing set placed our lite model among the top performing methods, while Xu-based classifier showed the lowest efficiency in separating IPF from control individuals.…”
Section: Discussionsupporting
confidence: 76%
“…32 Also, a set of five genes are candidates as biomarkers for KD diagnosing, namely, the HLA-DQB1, HLA-DRA, ZBTB48, TNFRSF13C, and CASD1. 33 Neurological involvement in Kawasaki disease Neurologic involvement in KD is reported in 1.1% to 3.7% of KD cases. 34 It ranges from common symptoms like irritability and lethargy to multi-system involvement such as seizures, myositis, sterile infectious diseases, facial palsy, hemiplegia, and coma.…”
Section: Biomarkersmentioning
confidence: 99%
“…33 Also, a set of five genes are candidates as biomarkers for KD diagnosing, namely, the HLA-DQB1, HLA-DRA, ZBTB48, TNFRSF13C, and CASD1. 34…”
Section: Biomarkersmentioning
confidence: 99%