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IJLai Transactions on Science and Engineering 2024年第3期2卷 66-71页

作者： Mirza Abdul Aleem Baig Center for biomedical Engineering School of Information Science and Technology University of Science and Technology of China

Context:bioinspired Artificial Intelligence(bio-ai)has emerged as a transformative tool in biomedical research,addressing challenges in cell labeling essential for understanding cellular behavior and *** cell labeling methods often struggle with accuracy,scalability,and adaptability in complex ***:This paper theoretically explores the integration of bio-ai models into cell labeling *** aims to assess how these models C encompassing neural networks,swarm intelligence,evolutionary algorithms,and self-organizing maps C can enhance cell identification and ***:The paper examines various bio-ai models that mimic biological processes such as neural functioning,swarm behavior,and evolutionary *** also evaluates the application of multimodal ai systems that combine imaging data with molecular and genetic *** addition,the potential of dynamic cell labeling,inspired by neural plasticity,is ***:The integration of bio-ai models has demonstrated significant improvements in accuracy,adaptability,and scalability for cell *** labeling systems minimize human error and enhance *** advancements in multimodal ai systems have shown promise in combining imaging with genetic and molecular data,providing more comprehensive insights into cellular *** labeling models inspired by neural plasticity offer enhanced tracking of cellular transitions over ***:bio-ai holds transformative potential in biomedical research via enabling real-time,dynamic labeling,essential for tracking cellular changes over time in processes like cancer progression,stem cell differentiation,and immune *** continued evolution of these ai-driven approaches is expected to accelerate breakthroughs in understanding diseases,tissue engineering,and regenerative medicine.

关键词： bioinspired Artificial Intelligence biomedical Research Cell Labeling bio-ai models

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