Nonnegative matrix factorization analysis and multiple machine learning methods identified IL17C and ACOXL as novel diagnostic biomarkers for atherosclerosis

Background Atherosclerosis is the common pathological basis for many cardiovascular and cerebrovascular diseases. The purpose of this study is to identify the diagnostic biomarkers related to atherosclerosis through machine learning algorithm. Methods Clinicopathological parameters and transcriptomi...

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Veröffentlicht in:	BMC bioinformatics Jg. 24; H. 1; S. 196 - 14
Hauptverfasser:	Rao, Li, Peng, Bo, Li, Tao
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	London BioMed Central 12.05.2023 BioMed Central Ltd Springer Nature B.V BMC
Schlagworte:	ACOXL Algorithms Arteriosclerosis Atherosclerosis Atherosclerosis - diagnosis Atherosclerosis - genetics Bioinformatics Biological markers Biomarkers Biomedical and Life Sciences Calibration Cell growth Cell proliferation Cerebrovascular diseases Computational Biology/Bioinformatics Computer Appl. in Life Sciences Correlation analysis Datasets Decision analysis Decision trees Diagnosis Diagnostic systems Epithelial cells Epithelium Factorization Gene expression Genes Genetic aspects Humans IL17C Immune infiltration Ischemia Learning algorithms Life Sciences Machine Learning Medical diagnosis Medical prognosis Medical research Medicine, Experimental Microarrays Mitochondria Performance prediction Prediction models Prognosis Random Forest Support vector machines Survival analysis Transcriptomics Vascular diseases China ACOXL IL17C Atherosclerosis Machine learning Immune infiltration
ISSN:	1471-2105, 1471-2105
Online-Zugang:	Volltext
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