基于机器学习和深度学习的抗菌肽预测研究进展

作者

  • 耿 浩宸 北京工商大学,计算机与人工智能学院,北京 100048 作者

DOI:

https://doi.org/10.52810/FAAI.2024.005

关键词:

人工智能, 深度学习, 机器学习, 抗菌肽, 预测

摘要

随着抗生素耐药性问题的日益严峻,全球对新型药物的需求急剧增加。抗菌肽,作为一种具有广谱抗菌活性的天然肽类物质,展现出对抗耐药性细菌的潜力。然而,传统的抗菌肽发现方法耗时耗力且效率低下,难以满足迅速发展的医疗需求。近年来,机器学习和深度学习技术在生物信息学和序列分析中的应用为抗菌肽的高效预测和设计提供了新的解决方案。本文综述了机器学习和深度学习在抗菌肽预测中的应用,从抗生素耐药性问题引出抗菌肽的重要性,介绍了抗菌肽预测的挑战,以及基于机器学习和深度学习的预测模型和性能评估。尽管面临诸多挑战,二者在抗菌肽研究中的应用仍具有巨大潜力,有望为解决抗生素耐药性问题提供新的策略。

作者简历

  • 作者
    耿浩宸,2022年入学北京工商大学计算机科学与技术专业。研究方向为人工智能与机器学习、网络安全以及计算机视觉等。

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2024-06-15

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浩宸耿. (2024). 基于机器学习和深度学习的抗菌肽预测研究进展. 人工智能前沿与应用, 1(1), 54-68. https://doi.org/10.52810/FAAI.2024.005