activity
20192026
most citedAI-powered virtual tissues from spatial proteomics for clinical diagnostics and biomedical discovery

10 citations · 20 across the 5 of their papers we have counts for

collaborators

6 papers

cs.CV2026

spora: A Unified Multimodal Dataset and Platform for Spatial Proteomics

Benedikt von Querfurth, Johann Wenckstern, Eeshaan Jain +9

Spatial proteomics (SP) captures the molecular composition and spatial organization of tissues at single-cell resolution, opening a direct view of the cellular ecosystems that driv…

cs.CL2025

Towards Scalable and Cross-Lingual Specialist Language Models for Oncology

Morteza Rohanian, Tarun Mehra, Nicola Miglino +3

Clinical oncology generates vast, unstructured data that often contain inconsistencies, missing information, and ambiguities, making it difficult to extract reliable insights for d…

q-bio.QM2025★ 10 cited

AI-powered virtual tissues from spatial proteomics for clinical diagnostics and biomedical discovery

Johann Wenckstern, Eeshaan Jain, Yexiang Cheng +9

Spatial proteomics technologies have transformed our understanding of complex tissue architecture in cancer but present unique challenges for computational analysis. Each study use…

cs.LG2024

Learning Personalized Treatment Decisions in Precision Medicine: Disentangling Treatment Assignment Bias in Counterfactual Outcome Prediction and Biomarker Identification

Michael Vollenweider, Manuel Schürch, Chiara Rohrer +3

Precision medicine has the potential to tailor treatment decisions to individual patients using machine learning (ML) and artificial intelligence (AI), but it faces significant cha…

stat.ML2024★ 6 cited

Towards AI-Based Precision Oncology: A Machine Learning Framework for Personalized Counterfactual Treatment Suggestions based on Multi-Omics Data

Manuel Schürch, Laura Boos, Viola Heinzelmann-Schwarz +4

AI-driven precision oncology has the transformative potential to reshape cancer treatment by leveraging the power of AI models to analyze the interaction between complex patient ch…

cs.LG2019★ 4 cited

Unsupervised Extraction of Phenotypes from Cancer Clinical Notes for Association Studies

Stefan G. Stark, Stephanie L. Hyland, Melanie F. Pradier +5

The recent adoption of Electronic Health Records (EHRs) by health care providers has introduced an important source of data that provides detailed and highly specific insights into…