Gabriel Dernbach

Humboldt-Universität zu Berlin — Germany

Medicine · Health Sciences

8h-index218citations29works5.32yr avg
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Research Topics

Radiomics and Machine Learning in Medical Imaging(11), Single-cell and spatial transcriptomics(6), AI in cancer detection(5), Cancer Cells and Metastasis(4), Bladder and Urothelial Cancer Treatments(4)

Publications29 total

ADC target profiling in NSCLC: Generalizable AI separates TROP-2 and cMET phenotypes
Zenodo (CERN European Organization for Nuclear Research)·2026Open Access
ADC target profiling in NSCLC: Generalizable AI separates TROP-2 and cMET phenotypes
Zenodo (CERN European Organization for Nuclear Research)·2026Open Access
MapPFN: Learning Causal Perturbation Maps in Context
ArXiv.org·2026Open Access
MapPFN: Learning Causal Perturbation Maps in Context
arXiv (Cornell University)·2026Open Access
Multiregional Immune Profiling Reveals Prognostic Patterns in Bladder Cancer
European Urology Oncology·2025Open Access
AI-powered spatial cell phenomics enhances risk stratification in non-small cell lung cancer
Nature Communications·2025· 3 citedOpen Access
Neural interaction explainable AI predicts drug response across cancers
NAR Cancer·2025· 2 citedOpen Access
Enhancing prognostic precision in bladder cancer: AI-driven tumor microenvironment analysis from H&E images.
Journal of Clinical Oncology·2025
Abstract 3351: From bench to bedside: generalizable AI model for ADC biomarker evaluation in NSCLC
Cancer Research·2025
Combining spatial transcriptomics and ECM imaging in 3D for mapping cellular interactions in the tumor microenvironment
Cell Systems·2025· 13 citedOpen Access
Spatial Expression of HER2, NECTIN4, and TROP-2 in Muscle-Invasive Bladder Cancer and Metastases: Implications for Pathological and Clinical Management
Modern Pathology·2025· 20 citedOpen Access
A0507 – Intratumoral heterogeneity of NECTIN4, HER2, and TROP-2 in muscle-invasive bladder cancer: Implications for pathological and clinical management
European Urology·2025Open Access
Decoding pan-cancer treatment outcomes using multimodal real-world data and explainable artificial intelligence
Nature Cancer·2025· 32 citedOpen Access
Atlas: A Novel Pathology Foundation Model by Mayo Clinic, Charité, and Aignostics
arXiv (Cornell University)·2025· 3 citedOpen Access
xCG: Explainable Cell Graphs for Survival Prediction in Non-Small Cell Lung Cancer
arXiv (Cornell University)·2024Open Access
Dissecting AI-based mutation prediction in lung adenocarcinoma: A comprehensive real-world study
European Journal of Cancer·2024· 9 citedOpen Access
Combining spatial transcriptomics and ECM imaging in 3D for mapping cellular interactions the tumor microenvironment
Zenodo (CERN European Organization for Nuclear Research)·2024Open Access
Abstract 5221: TTF-1 status in early-stage lung adenocarcinoma is an independent predictor of relapse and survival superior to tumor grading
Cancer Research·2024
Abstract 5222: AI-driven, mIF-based cell-omics reveals spatially resolved cell signature for outcome prediction in NSCLC patients
Cancer Research·2024
RudolfV: A Foundation Model by Pathologists for Pathologists
arXiv (Cornell University)·2024· 24 citedOpen Access
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Frequent Co-authors

Frederick Klauschen(22), Simon Schallenberg(20), Lukas Ruff(13), David Horst(11), Philipp Jurmeister(10), Kai Standvoss(10), Ulrich Keilholz(9), Sabine Merkelbach‐Bruse(8), Matthias Heldwein(8), Maximilian Alber(7), Mihnea P. Dragomir(7), Philipp Anders(7), Philipp Keyl(6), Klaus‐Robert Müller(6), Georg Schlachtenberger(6), Alexander Quaas(6), Reinhard Büttner(6), Nikolaj Frost(6), Grégoire Montavon(5), Kyrill Boschung(5)