Hodgkin lymphoma: 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
• CD20 2
• CD19 10
• PD-1 9
• CD30 9
• PD-L1 2
• BCL-2 2
• STAT3 0
• CD47 1
• JAK 1
• MYC 2
• TP53 0
• ALK 0
• BTK 1
• CD22 0
• IL-6 0
• AKT 0
• CTLA-4 0
• mTOR 0
• PI3K 0
• PRMT5 1
• ABL1 0
• BCMA 0
• CD123 1
• CD38 0
• EZH2 0
• METTL3 0
• NOTCH1 0
• NRF2 0
• TIM-3 0
• AMPK 0
• AXL 0
• BDNF 1
• CCR5 0
• CDK4/6 0
• CXCR4 0
• FGFR 0
• FLT3 0
• GLP-1 0
• HDAC 0
• IL-15 1
• IL-17 0
• MET 0
• MMP-9 0
• NLRP3 0
• PARP 0
• PRAME 0
• SGLT2 0
• SIRT1 0
• TERT 0
• TIGIT 0
• TLR4 0
• TNF 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 5
• Mendelian Randomization Analysis 6
• CRISPR-Cas Systems 1
• Gene Expression Profiling 14
• Whole Genome Sequencing 1
• Exome Sequencing 3
• Proteomics 5
• Metabolomics 1
• Flow Cytometry 9
• Immunohistochemistry 19
• Liquid Biopsy 1
• Circulating Tumor DNA 6
• Xenograft Model Antitumor Assays 20
• Positron-Emission Tomography 21
• Magnetic Resonance Imaging 18
• Machine Learning 11
• Deep Learning 3
• Cohort Studies 30
• Genome-Wide Association Study 7

• Development and internal validation of a machine learning model for predicting second primary malignancy risk in hodgkin lymphoma survivors: a population-based competing risk analysis.

  Hematology, Sept 2026

• A Risk Prediction Model Based on Combined CBC and CPD Parameters for Triage of Non-Hodgkin Lymphoma Inpatients.

  Ann Clin Lab Sci, Jul 2026

• Machine learning-driven investigation on liquid-liquid phase separation-related prognostic signature in diffuse large B-cell lymphoma.

  Br J Haematol, Jun 2026

• An interpretable machine learning model integrating [18F]FDG PET/CT radiomics and clinical features for predicting perforation following chemotherapy in gastrointestinal lymphoma: a multicenter study.

  Eur J Nucl Med Mol Imaging, May 2026

• Methodological illustration using machine learning methods to predict cancer-related fatigue in cancer patients.

  J Behav Med, May 2026

• Predicting survival of Hodgkin lymphoma using machine learning-an analysis based on the SEER database.

  Ann Hematol, Apr 2026

• Machine learning based on clinical and gene expression data assists in survival prediction and treatment optimization for diffuse large B-Cell lymphoma patients.

  Ann Hematol, Feb 2026

• Machine learning-driven dynamic prognosis for primary colorectal lymphoma.

  Sci Rep, Jan 2026

• Machine learning model of clinical laboratory data for 30-day mortality of patients with hodgkin's lymphoma in ICU: a retrospective study based on MIMIC-IV database.

  Clin Exp Med, Dec 2025

• ¹⁸F-FDG PET radiomics and machine learning for virtual biopsy and treatment decisions in lymphoma: a multicenter study.

  Phys Eng Sci Med, Nov 2025

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