Depression: 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
• BDNF 1
• NMDA 7
• NLRP3 0
• GLP-1 1
• TNF 0
• IL-6 3
• NRF2 0
• PPAR 0
• AMPK 0
• mTOR 0
• STAT3 0
• AKT 0
• JAK 1
• TLR4 0
• ERK 0
• IL-1 0
• PI3K 0
• AQP4 0
• HDAC 0
• HMGB1 0
• METTL3 0
• MMP-9 0
• SGLT2 0
• SIRT1 0
• VEGF 0
• BACE1 0
• C9orf72 0
• CCR5 0
• CD19 0
• CD22 0
• COX-2 0
• CTNNB1 0
• CXCR4 0
• DPP-4 0
• FOXP3 0
• HSP90 0
• IDO1 0
• IL-15 0
• IL-17 0
• IL-4 1
• MAPK 0
• MEK 0
• PARP 0
• PDGFR 0
• PINK1 0
• PRMT5 0
• RANKL 0
• SNCA 0
• STING 0
• TP53 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 14
• Mendelian Randomization Analysis 82
• CRISPR-Cas Systems 1
• Gene Expression Profiling 60
• Sequence Analysis, RNA 4
• Whole Genome Sequencing 1
• Exome Sequencing 4
• Proteomics 25
• Metabolomics 21
• Mass Spectrometry 2
• Flow Cytometry 1
• Immunohistochemistry 1
• Positron-Emission Tomography 25
• Magnetic Resonance Imaging 453
• Machine Learning 106
• Deep Learning 20
• Cohort Studies 125
• Genome-Wide Association Study 147

• Predicting remission following repetitive transcranial magnetic stimulation in treatment-resistant depression: a comparison of parsimonious and ensemble machine learning models.

  J Affect Disord, Oct 2026

• Transcriptional signatures associated with cortical similarity network changes in adolescents with major depressive disorder.

  Psychol Med, Oct 2026

• EDIT-B consortium: an external pan-European validation of an innovative machine learning-based test for bipolar disorder diagnosis.

  BMJ Ment Health, Oct 2026

• Machine learning-based source-level EEG functional network biomarkers of antidepressant treatment response in major depressive disorder.

  Prog Neuropsychopharmacol Biol Psychiatry, Oct 2026

• Quantifying Subject-Identity Variance in Spectral EEG Features and Its Role in Machine Learning Evaluation Leakage.

  Sensors (Basel), Sept 2026

• From real-world experience to precision prescribing of antidepressants: a machine-learning analysis of clinicians' treatment choices.

  Eur Neuropsychopharmacol, Sept 2026

• Multimodal machine learning model integrating electroencephalography, heart rate variability and clinical measures for distinguishing major psychiatric disorders.

  J Affect Disord, Sept 2026

• Toward precision acupuncture for depression: EEG microstate biomarkers and explainable machine learning for TECAS response.

  J Affect Disord, Sept 2026

• ATR-FTIR spectroscopic analysis and two-stage machine learning for the differentiation of bipolar and major depressive disorder.

  J Psychiatr Res, Sept 2026

• Predicting early psychiatric readmission among people with major depressive disorder: A machine learning analysis from the prospective multicentre DEEP READ study.

  J Affect Disord, Sept 2026

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