Author

Nona Farbehi

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Open access Aug 2026

SemanticST: A Scalable Multi‐Contextual Graph Learning Framework for Uncovering Spatial Niches and Robust Multi‐Sample Integration in Spatial Transcriptomics

ABSTRACT Spatial transcriptomics (ST) analysis is often hindered by technical limitations and methodological biases that let dominant signals overshadow subtle but crucial biological patterns, such as rare cell types and fine‐grained heterogeneity. This is especially true for high‐complexity datasets from platforms lik...

Roxana Zahedi, A. Argha, Nona Farbehi et al. · Advancement of science · 0 citations

#machine learning Apr 2026

Transcriptomic Models for Immunotherapy Response Prediction Show Limited Cross-cohort Generalisability

Immune checkpoint inhibitors (ICIs) have transformed cancer therapy; yet substantial proportion of patients exhibit intrinsic or acquired resistance, making accurate pre-treatment response prediction a critical unmet need. Transcriptomics-based biomarkers derived from bulk and single-cell RNA sequencing (scRNA-seq) off...

Yu-Heng Liang, Lucy Chhuo, A. Argha et al. · arXiv.org · 0 citations

Preprint Jul 2026

Harmonised benchmarking of foundation models for single-cell and spatial transcriptomics reveals context-dependent generalisation

This benchmark provides practical guidance for model selection and argues that future models should be judged by biological generalisation, interpretability and perturbation-grounded validity, not by scale or leaderboard performance alone.

S. Chen, Roxana Zahedi, Lucy Chhuo et al. · 0 citations