ArticleFrontiers in neurology2024
Using a k-means clustering to identify novel phenotypes of acute ischemic stroke and development of its Clinlabomics models.
Article in Frontiers in neurology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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10 citing papers in PubMed, 14 citations in OpenAlex.
- Symptom clustering of old adult dysphagic patient phenotypes by unsupervised machine learning using multidimensional characteristics: a cross-sectional study.BMC geriatrics · 2026Article
- Early hypoxia-induced secretome remodeling reveals adaptive mechanisms and biomarkers of blood-brain barrier dysfunction in ischemic stroke.Molecular brain · 2026Article
- Identification of key metabolic indicators associated with the comorbidity of ischemic stroke and diabetes mellitus using an optimal interpretable clinlabomics model.Frontiers in cardiovascular medicine · 2026Article
- Integrative Phenotyping of Knee Osteoarthritis: Linking WOMAC Cut-Offs, Kellgren-Lawrence Grades, and Cluster Analysis for Personalized Care.Life (Basel, Switzerland) · 2025Article
- Development and Validation of a Clinlabomics-Based Nomogram for Predicting the Prognosis of Small Cell Lung Cancer in China: A Multicenter, Retrospective Cohort Study.Cancer medicine · 2025Observational
- Subtype identification of clinical and thrombus imaging features in acute ischemic stroke: using clustering analysis and principal component analysis.Scientific reports · 2025Article
- Untargeted metabolomics unveils critical metabolic signatures in novel phenotypes of acute ischemic stroke.Metabolic brain disease · 2025Article
- Prognostic significance of the C-reactive protein-albumin-lymphocyte index and the pan-immune-inflammation value in ischemic and hemorrhagic stroke: a comparative analysis of subtypes.Frontiers in neurology · 2025Article
- The Use of AI for Phenotype-Genotype Mapping.Methods in molecular biology (Clifton, N.J.) · 2025Article
- Identifying metabolic parameters as key indicators of hyperuricemia and ischemic stroke comorbidity via interpretable Clinlabomics models.Frontiers in endocrinology · 2025Article
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Authors and funding
6 authors at 1 institution in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
Objective: Acute ischemic stroke (AIS) is a heterogeneous condition. To stratify the heterogeneity, identify novel phenotypes, and develop Clinlabomics models of phenotypes that can conduct more personalized treatments for AIS. Methods: In a retrospective analysis, consecutive AIS and non-AIS inpatients were enrolled. An unsupervised k-means clustering algorithm was used to classify AIS patients into distinct novel phenotypes. Besides, the intergroup comparisons across the phenotypes were performed in clinical and laboratory data. Next, the least absolute shrinkage and selection operator (LASSO) algorithm was used to select essential variables. In addition, Clinlabomics predictive models of phenotypes were established by a support vector machines (SVM) classifier. We used the area under curve (AUC), accuracy, sensitivity, and specificity to evaluate the performance of the models. Results: Of the three derived phenotypes in 909 AIS patients [median age 64 (IQR: 17) years, 69% male], in phenotype 1 ( Conclusion: In this study, three novel phenotypes that reflected the abnormal variables of AIS patients were identified, and the Clinlabomics models of phenotypes were established, which are conducive to individualized treatments.
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