Artificial intelligence in hematopoietic stem cell research and associated malignancies: From disease modeling to cell manufacturing

Engineering notes

Key topics: autonomous driving, reinforcement learning. See the paper for implementation details and experimental results.

Chinese explanation / 中文解读

中文解读待补充：本站会优先为端到端自动驾驶、BEV感知、3D目标检测、轨迹预测、路径规划、LiDAR感知等高价值论文补充中文说明。

Original abstract

Hematopoietic stem cells (HSCs) occupy the apex of the blood cell hierarchy, and artificial intelligence (AI) is fundamentally reshaping how their biology is decoded from normal self-renewal to malignant transformation and clinical transplantation.Trajectory inference algorithms applied to single-cell multi-omics have resolved continuous HSC differentiation with lineage priming detectable at the single-cell level, while convolutional neural networks trained on chromatin imaging predict HSC biological age and detect epigenetic rejuvenation signatures.In leukemic stem cell (LSC) biology, multiomics deep learning models map treatment-resistant quiescent LSC subclones, detect minimal residual disease with an area under the curve of 0.97 and predict venetoclax sensitivity in LSC-enriched niches.Deep learning also maps myeloma stem cell niche interactions and spatial heterogeneity in bone marrow biopsies.Virtual screening powered by AI speeds up LSC-targeted drug discovery, and reinforcement learning and digital twin models improve ex vivo HSC manufacturing and industrial-scale production of chimeric antigen receptor-T cells.In transplantation, natural language processing extraction and hybrid models classify risk for graft-vs-host disease into clinically relevant subgroups.Overall, top-performing AI models serve as computational surrogates for stemness biology but challenges in dataset diversity, interpretability and regulatory compliance need to be addressed before being used in a clinical setting.

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