ReviewArteriosclerosis, thrombosis, and vascular biology2023
Phenomics and Robust Multiomics Data for Cardiovascular Disease Subtyping.
Review in Arteriosclerosis, thrombosis, and vascular biology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
14 citing papers in PubMed.
- Thrombus Metabolism-Based Molecular Subtyping for Prognostic Risk Stratification in Acute Ischemic Stroke: A Preliminary Study.CNS neuroscience & therapeutics · 2026Article
- Joint clinical and molecular subtyping of COPD with variational autoencoders.Nature communications · 2026Article
- A metabolic-inflammatory phenotype of pelvic floor dysfunction: A machine learning-based cross-sectional study in a nationally representative US population.The Journal of international medical research · 2026Article
- Precision cardiovascular medicine with big data and AI.NPJ digital medicine · 2026Review
- Targeting the Biology of Aging in Cerebrovascular Disease: Inflammation, Metabolism, Senescence, and Regeneration.International journal of molecular sciences · 2026Review
- AI-driven identification of nutrition-modulated biomarkers and drug targets for cardiovascular therapeutic mechanisms.Frontiers in pharmacology · 2026Review
- Utilizing multimodal artificial intelligence to advance cardiovascular diseases.Precision clinical medicine · 2025Review
- How pharmacology can aid in the diagnosis of mental disorders.Naunyn-Schmiedeberg's archives of pharmacology · 2025Review
- HIF‑1α overexpression improves the efficacy of human induced pluripotent stem cell‑derived cardiomyocytes for cardiac repair following myocardial infarction.Molecular medicine reports · 2025Article
- Precision cardiovascular medicine: shifting the innovation paradigm.Frontiers in science · 2025Article
- Cancer phenomics research hotspots and development trends: a bibliometric analysis from 2000 to 2023.Discover oncology · 2024Article
- TACCO: Task-guided Co-clustering of Clinical Concepts and Patient Visits for Disease Subtyping based on EHR Data.KDD : proceedings. International Conference on Knowledge Discovery & Data Mining · 2024Article
- Integrating Computational and Biological Hemodynamic Approaches to Improve Modeling of Atherosclerotic Arteries.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Review
- Multi-Omics and Single-Cell Omics: New Tools in Drug Target Discovery.Arteriosclerosis, thrombosis, and vascular biology · 2024Article
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
The complex landscape of cardiovascular diseases encompasses a wide range of related pathologies arising from diverse molecular mechanisms and exhibiting heterogeneous phenotypes. This variety of manifestations poses significant challenges in the development of treatment strategies. The increasing availability of precise phenotypic and multiomics data of cardiovascular disease patient populations has spurred the development of a variety of computational disease subtyping techniques to identify distinct subgroups with unique underlying pathogeneses. In this review, we outline the essential components of computational approaches to select, integrate, and cluster omics and clinical data in the context of cardiovascular disease research. We delve into the challenges faced during different stages of the analysis, including feature selection and extraction, data integration, and clustering algorithms. Next, we highlight representative applications of subtyping pipelines in heart failure and coronary artery disease. Finally, we discuss the current challenges and future directions in the development of robust subtyping approaches that can be implemented in clinical workflows, ultimately contributing to the ongoing evolution of precision medicine in health care.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.