Melanoma: 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
• PD-1 55
• BRAF 18
• PD-L1 22
• MEK 7
• TP53 0
• CTLA-4 10
• MAPK 2
• NRAS 8
• STING 0
• STAT3 0
• PRAME 3
• AKT 0
• PI3K 0
• BAP1 2
• VEGF 4
• mTOR 0
• ERK 0
• LAG-3 2
• CTNNB1 0
• RAS 2
• CD40 0
• CDKN2A 3
• TNF 0
• YAP 0
• PTEN 1
• JAK 0
• KIT 3
• KRAS 1
• JNK 0
• PPAR 0
• TIGIT 0
• ALK 0
• B7-H3 3
• BCL-2 0
• EZH2 0
• HIF-1 0
• IL-15 1
• NF1 2
• NLRP3 0
• NRF2 0
• AMPK 0
• HSP90 0
• CD47 0
• MYC 0
• PKM2 0
• AXL 1
• COX-2 0
• EGFR 0
• GPX4 0
• HDAC 1
• IDH 1
• IL-17 1
• MET 1
• MMP-9 0
• CDK4/6 0
• CXCR4 0
• FGFR 0
• HER2 3
• IGF1R 0
• PSMA 3

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

12 of 12+ institutions, ordered by recent publications and open trials. This is not a ranking of quality.

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

• A Study of Navlimetostat (BMS-986504) Monotherapy and in Combination With Other Agents in Participants With Advanced and/or Metastatic Solid Tumors With Homozygous MTAP Deletion (MountainTAP-5)

  NCT07492680· Bristol-Myers Squibb· Source

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

• Single-Cell Analysis 51
• Organoids 8
• Mendelian Randomization Analysis 12
• CRISPR-Cas Systems 21
• Gene Expression Profiling 116
• Sequence Analysis, RNA 9
• Whole Genome Sequencing 6
• Exome Sequencing 7
• Proteomics 46
• Metabolomics 20
• Mass Spectrometry 9
• Flow Cytometry 65
• Immunohistochemistry 106
• Liquid Biopsy 12
• Circulating Tumor DNA 37
• Xenograft Model Antitumor Assays 120
• Positron-Emission Tomography 31
• Magnetic Resonance Imaging 56
• Machine Learning 47
• Deep Learning 44
• Cohort Studies 77
• Genome-Wide Association Study 22

• Pre-treatment machine learning prediction of local failure and time to local failure after Gamma Knife radiosurgery for radioresistant brain metastases.

  J Neurooncol, Nov 2026

• Burden of malignant melanoma among the elderly population in East Asia from 1990 to 2023 and machine learning projection to 2040.

  PLoS One, Sept 2026

• Predicting Targeted and Immunotherapeutic Response Outcomes in Melanoma With Single-Cell Raman Spectroscopy and Artificial Intelligence.

  JCO Precis Oncol, Sept 2026

• Explainable AI-Assisted Label-Free Raman Biosensing Reveals Therapy-Associated Spectral Signatures in Melanoma Tumors.

  Biosensors (Basel), Sept 2026

• A multiscale geospatial analysis integrating explainable machine learning to characterize spatial disparities in melanoma mortality associated with environmental justice and healthcare access in the contiguous United States.

  Front Public Health, Aug 2026

• Development of a novel RNA modification-based risk model to predict prognosis and immunotherapy response in melanoma using machine learning.

  J Cancer Res Clin Oncol, Aug 2026

• Automated model-based lesion tracking in CT: colorectal liver metastases as a use case for development and performance analysis.

  Eur Radiol Exp, Jul 2026

• FFixR: a machine learning framework for accurate somatic mutation calling from FFPE RNA-seq data in cancer.

  Bioinformatics, Jul 2026

• Identification of cell pyroptosis-related gene signature for prognosis in skin cutaneous melanoma.

  Comput Biol Chem, Jun 2026

• MLMarker: a machine learning framework for tissue inference and biomarker discovery.

  Genome Biol, Jun 2026

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