ArticleScientific reports2024
Discovering biomarkers associated and predicting cardiovascular disease with high accuracy using a novel nexus of machine learning techniques for precision medicine.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 56 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
56 citing papers in PubMed, 814 citations in OpenAlex.
- A pilot randomised control study to investigate the effect of the South African Adolescence Group Sleep Intervention (SAASI) on adolescent sleep and PTSD.European journal of psychotraumatology · 2024Trial
- Wastewater treatment configuration shapes ESBL- and KPC-producing gram-negative bacteria in effluents from a Colombian city: a one health perspective.Journal of water and health · 2026Article
- Polydopamine-Based Molecular Imprinting Polymer Electrochemical Sensor for Neopterin Detection.Bioengineering (Basel, Switzerland) · 2026Article
- Mapping the Digital Mind: A Meta-Analysis of EEG Biomarkers in Cognition, Emotion, and Mental Health.Brain sciences · 2026Review
- Time and person sensitive foundation model for disease prediction and risk stratification.NPJ digital medicine · 2026Article
- Systematic bibliometric and visualized analysis of global research trends, impact, emerging areas, and hotspots of artificial intelligence in personalized medicine.Naunyn-Schmiedeberg's archives of pharmacology · 2026Article
- Comment on, "Long-term outcome of tailored antithrombotic therapy based on platelet function testing in patients undergoing percutaneous coronary intervention: a 5-year retrospective cohort study".Heart and vessels · 2026Article
- Single Cell RNA Sequencing in Colorectal Cancer Immunology: Recent Updates, Application, and Emerging Challenges.Current molecular medicine · 2026Review
- The Gap Between Suspected Local Anesthetic Allergy and Formal Diagnostic Evaluation.Journal of asthma and allergy · 2026Article
- From acute diagnosis to longitudinal risk stratification: a paradigm shift in the clinical role of cardiac biomarkers.Frontiers in cardiovascular medicine · 2026Article
- Graph Neural Networks Reveal Candidate Protein Biomarkers Underlying Abdominal Aortic Aneurysm Biology.Computational and structural biotechnology journal · 2026Article
- Integrating Artificial Intelligence with Global Genomic Resources: A Narrative Review of Implications for Precision Medicine.Journal of multidisciplinary healthcare · 2026Review
- Unveiling Heart Failure: Gender-Specific Insights and Innovations in Women's Cardiac Health.Current pharmaceutical design · 2026Review
- Web-based cardiovascular disease risk prediction using machine learning.Frontiers in artificial intelligence · 2026Article
- The Emerging Roles of Resolvins: Potential Diagnostic Biomarkers for Cardiovascular Diseases.Current cardiology reviews · 2026Review
- FCGBP links hormonal imbalance and hepatic steatosis in PCOS-NAFLD comorbidity: an integrative bioinformatics and experimental study.Frontiers in endocrinology · 2026Article
- A comprehensive survey of artificial intelligence methods for cardiovascular disease detection: Recent advances and future challenges.MethodsX · 2025Review
- Performance evaluation of lateritic soil and fungi (Water science and technology : a journal of the International Association on Water Pollution Research · 2025Article
- YBX1 regulation of alveolar type II epithelial cells in idiopathic pulmonary fibrosis: mechanistic insights and small-molecule drug screening.Journal of translational medicine · 2025Article
- Cardiometabolic Biomarkers and Cardiovascular Risk Stratification in Polish Military Personnel: A Chemometric Approach.International journal of molecular sciences · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Personalized interventions are deemed vital given the intricate characteristics, advancement, inherent genetic composition, and diversity of cardiovascular diseases (CVDs). The appropriate utilization of artificial intelligence (AI) and machine learning (ML) methodologies can yield novel understandings of CVDs, enabling improved personalized treatments through predictive analysis and deep phenotyping. In this study, we proposed and employed a novel approach combining traditional statistics and a nexus of cutting-edge AI/ML techniques to identify significant biomarkers for our predictive engine by analyzing the complete transcriptome of CVD patients. After robust gene expression data pre-processing, we utilized three statistical tests (Pearson correlation, Chi-square test, and ANOVA) to assess the differences in transcriptomic expression and clinical characteristics between healthy individuals and CVD patients. Next, the recursive feature elimination classifier assigned rankings to transcriptomic features based on their relation to the case-control variable. The top ten percent of commonly observed significant biomarkers were evaluated using four unique ML classifiers (Random Forest, Support Vector Machine, Xtreme Gradient Boosting Decision Trees, and k-Nearest Neighbors). After optimizing hyperparameters, the ensembled models, which were implemented using a soft voting classifier, accurately differentiated between patients and healthy individuals. We have uncovered 18 transcriptomic biomarkers that are highly significant in the CVD population that were used to predict disease with up to 96% accuracy. Additionally, we cross-validated our results with clinical records collected from patients in our cohort. The identified biomarkers served as potential indicators for early detection of CVDs. With its successful implementation, our newly developed predictive engine provides a valuable framework for identifying patients with CVDs based on their biomarker profiles.
Indexed as
Identifiers
What OpenQuestion holds
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.