arXiv Machine Learning By Shuyu Chen, Chen Zhu, Ye Zhang, Yang Li, Qiqi Xie, Haohan Wang

SCTA: An Agentic Framework for Stable and Interpretable Target Gene Discovery from Single-Cell RNA Sequencing

Read the original on arXiv Machine Learning →

arXiv:2607. 23821v1 Announce Type: new Abstract: Identifying therapeutic target genes from single-cell RNA sequencing (scRNA-seq) data remains a fundamental challenge in translational biology.

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arXiv AI
Aug 3

ELISA: An Interpretable Hybrid Generative AI Agent for Expression-Grounded Discovery in Single-Cell Genomics

arXiv:2603. 11872v3 Announce Type: replace-cross Abstract: Translating single-cell RNA sequencing (scRNA-seq) data into mechanistic biological hypotheses remains a critical bottleneck, as agentic AI systems lack direct access to transcriptomic representations while expression foundation models remain opaque to natural language.

By Omar Coser
arXiv Machine Learning
Sep 18

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

The study evaluated nine transcriptomic models—five bulk RNA‑seq and four single‑cell RNA‑seq—designed to predict response to immune checkpoint inhibitors. Across independent datasets, bulk models performed near chance while single‑cell models offered only modest gains, and pathway analyses revealed inconsistent biomarker signals. The results highlight the limited cross‑cohort robustness and biological consistency of current transcriptomic ICI predictors.

By Yuheng Liang, Lucy Chhuo, Ahmadreza Argha, Nona Farbehi, Lu Chen, Roohallah Alizadehsani, Mehdi Hosseinzadeh, Min Yang, Thantrira Porntaveetusm, Youqiong Ye, Hamid Alinejad-Rokny