ReviewAPL bioengineering2021
Application of machine learning in understanding atherosclerosis: Emerging insights.
Review in APL bioengineering, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 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
9 citing papers in PubMed.
- Interpretable machine learning analysis for relationships betweenFrontiers in cellular and infection microbiology · 2026Article
- An Unsupervised Learning Tool for Plaque Tissue Characterization in Histopathological Images.Sensors (Basel, Switzerland) · 2024Article
- Transcriptomic research in atherosclerosis: Unravelling plaque phenotype and overcoming methodological challenges.Journal of molecular and cellular cardiology plus · 2023Review
- A machine learning model for non-invasive detection of atherosclerotic coronary artery aneurysm.International journal of computer assisted radiology and surgery · 2022Article
- Heart failure-type symptom scores in chronic kidney disease: The importance of body mass index.International journal of obesity (2005) · 2022Article
- Novel Biomarkers of Atherosclerotic Vascular Disease-Latest Insights in the Research Field.International journal of molecular sciences · 2022Review
- Artificial intelligence in atherosclerotic disease: Applications and trends.Frontiers in cardiovascular medicine · 2022Review
- Identification of immune cell infiltration and diagnostic biomarkers in unstable atherosclerotic plaques by integrated bioinformatics analysis and machine learning.Frontiers in immunology · 2022Article
- The Inflamm-Aging Model Identifies Key Risk Factors in Atherosclerosis.Frontiers in genetics · 2022Article
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.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Biological processes are incredibly complex-integrating molecular signaling networks involved in multicellular communication and function, thus maintaining homeostasis. Dysfunction of these processes can result in the disruption of homeostasis, leading to the development of several disease processes including atherosclerosis. We have significantly advanced our understanding of bioprocesses in atherosclerosis, and in doing so, we are beginning to appreciate the complexities, intricacies, and heterogeneity atherosclerosi. We are also now better equipped to acquire, store, and process the vast amount of biological data needed to shed light on the biological circuitry involved. Such data can be analyzed within machine learning frameworks to better tease out such complex relationships. Indeed, there has been an increasing number of studies applying machine learning methods for patient risk stratification based on comorbidities, multi-modality image processing, and biomarker discovery pertaining to atherosclerotic plaque formation. Here, we focus on current applications of machine learning to provide insight into atherosclerotic plaque formation and better understand atherosclerotic plaque progression in patients with cardiovascular disease.
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.