ReviewJACC. Basic to translational science2017
Enabling Precision Cardiology Through Multiscale Biology and Systems Medicine.
Review in JACC. Basic to translational science, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 42 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
42 citing papers in PubMed.
- Detection of an anti-angina therapeutic module in the effective population treated by a multi-target drug Danhong injection: a randomized trial.Signal transduction and targeted therapy · 2021Trial
- Artificial Intelligence in Pulmonary Hypertension: Current State and Future Prospects.Reviews in cardiovascular medicine · 2026Review
- Echocardiographic Phenomapping in HFpEF: From Imaging Biomarkers to Precision Cardiovascular Medicine.Echocardiography (Mount Kisco, N.Y.) · 2026Review
- Multi-omic analysis of human PHACTR1 signaling networks.Communications biology · 2026Article
- Artificial intelligence in mitotic checkpoint modeling: transforming our understanding of cellular division through machine learning and predictive biology.Briefings in bioinformatics · 2026Review
- Enhanced prediction of left ventricular ejection fraction using electrocardiography with the addition of clinical metadata.The Korean journal of internal medicine · 2026Article
- A macrophage gene-regulatory network linked to clinical severity of coronary artery disease : The STARNET and NGS-PREDICT primary blood macrophage studies.Basic research in cardiology · 2025Article
- Digital Twin Models in Atrial Fibrillation: Charting the Future of Precision Therapy?Journal of personalized medicine · 2025Review
- Cardiovascular Imaging in the Era of Precision Medicine: Insights from Advanced Technologies - A Narrative Review.Health science reports · 2024Article
- Review
- Up digital and personal: How heart digital twins can transform heart patient care.Heart rhythm · 2024Review
- Recent Advances in Pharmaceutical Design: Unleashing the Potential of Novel Therapeutics.Current pharmaceutical biotechnology · 2024Review
- From 'Omics to Multi-omics Technologies: the Discovery of Novel Causal Mediators.Current atherosclerosis reports · 2023Review
- Digital medicine: the next big leap advancing cardiovascular science.BMC cardiovascular disorders · 2023Article
- The Capacity of Artificial Intelligence in COVID-19 Response: A Review in Context of COVID-19 Screening and Diagnosis.Diagnostics (Basel, Switzerland) · 2022Review
- Targeting the cytoskeleton and extracellular matrix in cardiovascular disease drug discovery.Expert opinion on drug discovery · 2022Review
- Using Deep-Learning Algorithms to Simultaneously Identify Right and Left Ventricular Dysfunction From the Electrocardiogram.JACC. Cardiovascular imaging · 2022Article
- Artificial Intelligence and Cardiovascular Genetics.Life (Basel, Switzerland) · 2022Review
- Machine Learning in Medicine: Review and Applicability.Arquivos brasileiros de cardiologia · 2022Review
- Connections for Matters of the Heart: Network Medicine in Cardiovascular Diseases.Frontiers in cardiovascular medicine · 2022Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The traditional paradigm of cardiovascular disease research derives insight from large-scale, broadly inclusive clinical studies of well-characterized pathologies. These insights are then put into practice according to standardized clinical guidelines. However, stagnation in the development of new cardiovascular therapies and variability in therapeutic response implies that this paradigm is insufficient for reducing the cardiovascular disease burden. In this state-of-the-art review, we examine 3 interconnected ideas we put forth as key concepts for enabling a transition to precision cardiology: 1) precision characterization of cardiovascular disease with machine learning methods; 2) the application of network models of disease to embrace disease complexity; and 3) using insights from the previous 2 ideas to enable pharmacology and polypharmacology systems for more precise drug-to-patient matching and patient-disease stratification. We conclude by exploring the challenges of applying a precision approach to cardiology, which arise from a deficit of the required resources and infrastructure, and emerging evidence for the clinical effectiveness of this nascent approach.
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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.