Τίτλος:
A computational workflow for the detection of candidate diagnostic biomarkers of Kawasaki disease using time-series gene expression data
Γλώσσες Τεκμηρίου:
Αγγλικά
Περίληψη:
Unlike autoimmune diseases, there is no known constitutive and disease-defining biomarker for systemic autoinflammatory diseases (SAIDs). Kawasaki disease (KD) is one of the “undiagnosed” types of SAIDs whose pathogenic mechanism and gene mutation still remain unknown. To address this issue, we have developed a sequential computational workflow which clusters KD patients with similar gene expression profiles across the three different KD phases (Acute, Subacute and Convalescent) and utilizes the resulting clustermap to detect prominent genes that can be used as diagnostic biomarkers for KD. Self-Organizing Maps (SOMs) were employed to cluster patients with similar gene expressions across the three phases through inter-phase and intra-phase clustering. Then, false discovery rate (FDR)-based feature selection was applied to detect genes that significantly deviate across the per-phase clusters. Our results revealed five genes as candidate biomarkers for KD diagnosis, namely, the HLA-DQB1, HLA-DRA, ZBTB48, TNFRSF13C, and CASD1. To our knowledge, these five genes are reported for the first time in the literature. The impact of the discovered genes for KD diagnosis against the known ones was demonstrated by training boosting ensembles (AdaBoost and XGBoost) for KD classification on common platform and cross-platform datasets. The classifiers which were trained on the proposed genes from the common platform data yielded an average increase by 4.40% in accuracy, 5.52% in sensitivity, and 3.57% in specificity than the known genes in the Acute and Subacute phases, followed by a notable increase by 2.30% in accuracy, 2.20% in sensitivity, and 4.70% in specificity in the cross-platform analysis. © 2021 The Authors
Συγγραφείς:
Pezoulas, V.C.
Papaloukas, C.
Veyssiere, M.
Goules, A.
Tzioufas, A.G.
Soumelis, V.
Fotiadis, D.I.
Περιοδικό:
Computational and Structural Biotechnology Journal
Λέξεις-κλειδιά:
Adaptive boosting; Classification (of information); Conformal mapping; Diagnosis; Gene expression; Self organizing maps, Boosting ensembles; Common platform; Computational workflows; Cross-platform; Diagnostic biomarkers; Disease diagnosis; Kawasaki; Kawasaki disease; Self-organizing map; Systemic autoinflammatory disease, Biomarkers, B cell activating factor receptor; B lymphocyte receptor; caspase 3; CD40 antigen; HLA DQB1 antigen; HLA DR antigen, adult; Article; CASD1 gene; controlled study; diagnostic accuracy; diagnostic test accuracy study; feature selection; functional genomics; gene expression; gene expression profiling; gene probe; human; immune response; marker gene; mucocutaneous lymph node syndrome; phylogenetic tree; RAW 264.7 cell line; sensitivity and specificity; time series analysis; ZBTB48 gene
DOI:
10.1016/j.csbj.2021.05.036
