Diabetic eye disease: who works on it

One card per institution. Filter by target, by country, or show only institutions in EU-13 (Horizon Europe widening) countries.

• All targets
• VEGF 19
• GLP-1 1
• NRF2 0
• NLRP3 1
• AMPK 0
• AKT 0
• SGLT2 1
• HIF-1 0
• PPAR 0
• PI3K 0
• mTOR 0
• STAT3 0
• MAPK 0
• STING 0
• SIRT1 0
• ERK 0
• GPX4 0
• IL-1 0
• TLR4 0
• YAP 0
• ACE2 0
• CTNNB1 0
• HMGB1 0
• IL-6 1
• MMP-9 0
• TNF 0
• TP53 0
• ANGPTL3 0
• BDNF 0
• CXCR4 0
• FTO 0
• GIP 0
• HDAC 0
• IL-17 0
• JAK 0
• MEK 0
• PDGFR 0
• PINK1 0
• APOE 0
• AQP4 0
• BAP1 0
• BCL-2 0
• CCR5 0
• CD40 0
• CDK4/6 0
• CDKN2A 0
• COX-2 0
• DPP-4 0
• EGFR 0
• FAK 0
• G6PD 0
• IL-15 0
• IL-4 0
• JNK 0
• MDM2 0
• METTL3 0
• MYC 0
• NMDA 0
• RPGR 0
• TDP-43 0

EU-13 is a filter, not a ranking: it only narrows the list.

No research groups match this view yet.

Trials whose registry record says individual participant data (IPD) will be shared.

No trial in this view states that it shares participant data.

Find publications on this disease by a MeSH method term, for example a sequencing or imaging method.

• Single-Cell Analysis 15
• Organoids 2
• Mendelian Randomization Analysis 26
• CRISPR-Cas Systems 4
• Gene Expression Profiling 34
• Sequence Analysis, RNA 3
• Exome Sequencing 2
• Proteomics 30
• Metabolomics 15
• Mass Spectrometry 2
• Flow Cytometry 5
• Immunohistochemistry 13
• Liquid Biopsy 1
• Xenograft Model Antitumor Assays 1
• Positron-Emission Tomography 1
• Magnetic Resonance Imaging 4
• Machine Learning 49
• Deep Learning 73
• Cohort Studies 47
• Genome-Wide Association Study 22

• [Construction of predictive models for type 2 diabetic retinopathy based on the principle of differentiation of syndrome elements].

  Nan Fang Yi Ke Da Xue Xue Bao, Oct 2026

• Machine Learning-Based Prediction of Albuminuria in Diabetic Patients Using Retinal Microvascular Metrics on Optical Coherence Tomography Angiography.

  Transl Vis Sci Technol, Oct 2026

• Current Trends in AI and Eye Disease Diagnostics.

  Ophthalmic Physiol Opt, Sept 2026

• Artificial intelligence-driven diabetic retinopathy research: mapping the evolution, coupling, and global collaboration landscape (1996-2026).

  Front Endocrinol (Lausanne), Sept 2026

• Development and validation of an explainable machine learning model for differentiating diabetic nephropathy from diabetic retinopathy in patients with type 2 diabetes.

  Front Endocrinol (Lausanne), Sept 2026

• Construction and Validation of a Long-Term Visual Prognosis Prediction Model for Proliferative Diabetic Retinopathy After Vitrectomy Based on Machine Learning.

  Endocrinol Diabetes Metab, Sept 2026

• Identification of comorbidity-based clusters and mortality risk in hospitalised patients with diabetes mellitus in Karachi, Pakistan: a machine learning analysis.

  BMJ Open, Aug 2026

• Extreme phenotype-derived machine learning reveals susceptibility and resilience signatures for severe diabetic retinopathy.

  Front Endocrinol (Lausanne), Aug 2026

• Emerging research trends in the application of artificial intelligence, LLMs, machine learning, and deep learning in ophthalmic diseases: A bibliometric and visual analysis.

  Photodiagnosis Photodyn Ther, Aug 2026

• ERFMTDA: Predicting tsRNA-disease associations using an enhanced rotative factorization machine.

  PLoS Comput Biol, Aug 2026

See which targets of this disease your abstract mentions, and go to the people working on them.