9 citations · 9 across the 13 of their papers we have counts for
6 papers · 1 filter
Large Language Model Agents for Evidence Based Genetic Disease Severity Classification
Tohid Ghasemnejad, Ahmadreza Argha, Mark Grosser +7
Disease severity classification for genetic conditions is subjective and labor-intensive, creating bottlenecks in genomic screening, where commercial panels vary widely in size and…
Harmonised benchmarking of foundation models for single-cell and spatial transcriptomics reveals context-dependent generalisation
Sally Chen, Roxana Zahedi, Lucy Chhuo +12
Single-cell and spatial foundation models promise transferable biological representations, yet their generality remains largely untested across modalities, biological domains and a…
Transcriptomic Models for Immunotherapy Response Prediction Show Limited Cross-cohort Generalisability
Yuheng Liang, Lucy Chhuo, Ahmadreza Argha +9
Immune checkpoint inhibitors (ICIs) have transformed cancer therapy; yet substantial proportion of patients exhibit intrinsic or acquired resistance, making accurate pre-treatment…
SemanticST: Spatially Informed Semantic Graph Learning for Clustering, Integration, and Scalable Analysis of Spatial Transcriptomics
Roxana Zahedi, Ahmadreza Argha, Nona Farbehi +4
Spatial transcriptomics (ST) technologies enable gene expression profiling with spatial resolution, offering unprecedented insights into tissue organization and disease heterogenei…
Interpretable graph-based models on multimodal biomedical data integration: A technical review and benchmarking
Alireza Sadeghi, Farshid Hajati, Ahmadreza Argha +3
Integrating heterogeneous biomedical data including imaging, omics, and clinical records supports accurate diagnosis and personalised care. Graph-based models fuse such non-Euclide…
Deep Learning in Spatially Resolved Transcriptomics: A Comprehensive Technical View
Roxana Zahedi Nasab, Mohammad Reza Eftekhariyan Ghamsari, Ahmadreza Argha +6
Spatially resolved transcriptomics (SRT) has evolved rapidly through various technologies, enabling scientists to investigate both morphological contexts and gene expression profil…