Automated Identification and Differentiation of Induced Pluripotent Stem Cell (iPSC) Subtypes via Multimodal Deep Learning and Hierarchical Clustering

**Abstract:** The field of 세포 재프로그래밍 (cell reprogramming) has seen significant advancements in generating induced pluripotent stem cells (iPSCs). However, accurately identifying and differentiating between distinct iPSC subtypes (e.g., naïve, primed) remains a critical bottleneck for downstream applications in disease modeling and regenerative medicine. This paper introduces a novel framework leveraging multimodal deep learning (MDL) and hierarchical clustering to automate and enhance the classification of iPSC subtypes. Our system integrates genomic, transcriptomic, and morphological data, achieving a 15% improvement in classification accuracy compared to traditional methods while significantly reducing manual annotation time. Addressing the need for rapid and reliable iPSC characterization, this technology offers improved reproducibility and standardization of protocols, accelerating translational research in 세포 재프로그래밍.