Biomedical engineering is a relatively young interdisciplinary field based on engineering, biology, and medicine. Of note, the rapid progress of artificial intelligence (AI)-based technologies has made a significant impact on the biomedical engineering field, and continuously bring innovations and breakthroughs. Recently, ChatGPT, an AI chatbot developed by OpenAI company, has gained tremendous attention due to its powerful natural language generation and understanding ability. In this study, we explored potential of GPT-4 in the eight branches of biomedical engineering including medical imaging, medical devices, bioinformatics, biomaterials, biomechanics, gene and cell engineering, tissue engineering, and neural engineering. Our results show that the application of GPT-4 will bring new opportunities for the development of this field.
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| http://dx.doi.org/10.1007/s10439-023-03221-1 | DOI Listing |
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Antibody generation requires the use of one or more time-consuming methods, namely animal immunization, and in vitro display technologies. However, the recent availability of large amounts of antibody sequence and structural data in the public domain along with the advent of generative deep learning algorithms raises the possibility of computationally generating novel antibody sequences with desirable developability attributes. Here, we describe a deep learning model for computationally generating libraries of highly human antibody variable regions whose intrinsic physicochemical properties resemble those of the variable regions of the marketed antibody-based biotherapeutics (medicine-likeness).
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