scLLM-DSC: LLM-Knowledge Enhanced Cross-Modal Deep Structural Clustering for Single-Cell RNA Sequencing
arXiv:2606.13007v1 Announce Type: cross Abstract: Clustering is fundamental to scRNA-seq analysis, serving as a cornerstone for identifying cell populations and resolving tissue heterogeneity. However, existing methods focus on mining numerical statistical patterns, suffering from semantic agnosticism by neglecting the intrinsic biological functions encoded by genes. While Large Language Models (LLMs) offer promising semantic capabilities, their direct adaptation to cell clustering is hindered by the structural mismatch between generative pre-training objectives and discriminative downstream t
The convergence of advanced large language models with specific scientific domains like single-cell genomics is a natural progression as LLM capabilities mature.
This development allows for a deeper and more biologically meaningful interpretation of complex genomic data, moving beyond purely statistical patterns to incorporate semantic understanding.
Biological discovery in areas like disease mechanisms and drug target identification can become significantly more sophisticated, driven by AI systems that understand both data and context.
- · Biotech companies
- · Pharmaceutical R&D
- · Genomic sequencing providers
- · AI-driven drug discovery platforms
- · Traditional bioinformatics software
- · Manual genomic analysis workflows
More accurate and faster identification of cell populations and disease biomarkers.
Accelerated development of precision medicines and targeted therapies based on AI-derived insights.
The integration of LLMs across other 'omics' data types, leading to a unified AI-driven biological knowledge layer.
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Read at arXiv cs.AI