ReviewBiomedicines2025
Integrating Machine Learning and Multi-Omics to Explore Neutrophil Heterogeneity.
Review in Biomedicines, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 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
6 citing papers in PubMed.
- Engineered Neutrophils in Translational Medicine: Gene Editing, Nanotechnology, and AI-Driven Clinical Breakthroughs.Advanced healthcare materials · 2026Review
- Machine learning identifies a NETs-related four-gene diagnostic signature for abdominal aortic aneurysm.Medicine · 2026Article
- MicroRNAs in Heart Failure Pathogenesis and Progression: Mechanistic Control, Biomarker Potential, and Translational Perspectives.Life (Basel, Switzerland) · 2026Review
- GeneCytNet: an interpretable deep learning framework for rheumatoid arthritis classification andFrontiers in immunology · 2026Article
- Identification of immunosuppressive neutrophils using multi-omics: why functional testing remains key.Frontiers in immunology · 2026Review
- Context-Dependent Roles of Siglec-FBiomedicines · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
13 authors.
Funding
Abstract
Traditionally considered as homogeneous innate immune cells, neutrophils are now found to exhibit phenotypic and functional heterogeneity. How to determine whether the functional changes of neutrophils are caused by activation or the result of gene reprogramming? Recent advances in multi-omics technologies, including genomics, transcriptomics, proteomics, metabolomics, and spatial omics, have comprehensively explained the mechanism of neutrophil heterogeneity. At the same time, artificial intelligence, especially machine learning, has promoted the in-depth analysis of multi-omics. Here, we introduce the latest progress in the discovery of neutrophil subsets by omics research. We will further discuss the application of machine learning in analyzing the heterogeneity of neutrophils through omics methods. Our goal is to provide a comprehensive overview of how machine learning and multi-omics are reshaping our understanding of neutrophil biology and pathophysiology.
Indexed as
Identifiers
What Socratic 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 Socratic graph.