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Más allá de la generación de texto: modelos de lenguaje grandes como agentes autónomos para orquestar ciclos de
Anqiang Ye1, Bing-Ying Wang1, Zhenshun Cheng2
1Department of Respiratory and Critical Care Medicine, Zhongnan Hospital of Wuhan University, School of Pharmaceutical Sciences, Wuhan University, Wuhan 430071, China; Key Laboratory of Combinatorial Biosynthesis and Drug Discovery, Ministry of Education, Wuhan University, Wuhan 430071, China.
Los modelos de lenguaje grandes (LLM) están revolucionando la biología sintética al permitir el análisis semántico de datos biológicos. Estos modelos aceleran el ciclo de diseño-construcción-prueba-aprendizaje, presentando nuevas oportunidades y desafíos para la ingeniería biológica.
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