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Shihori Tanabe¹, Sabina Quader², Ryuichi Ono³

¹Division of Risk Assessment, Center for Biological Safety and Research, National Institute of Health Sciences, 3-25-26, Tonomachi, Kawasaki-ku, Kawasaki 210-9501, Japan.

Current issues in molecular biology

|July 23, 2025

概括

机器学习模型预测了SARS-CoV-2感染中的冠状病毒发病路径激活. 这种使用途径图像的方法可能有助于确定严重急性呼吸系统综合征冠状病毒2的有效治疗方法.

科学领域:

病毒学病毒学
计算生物学计算生物学
生物信息学是一种生物信息学.

背景情况:

冠状病毒发病途径,包括干扰素和亡等信号级联,在严重急性呼吸系统综合征 (SARS) 冠状病毒感染期间被激活.
了解这些途径对于开发针对冠状病毒的向疗法至关重要,包括SARS-CoV-2.

研究的目的:

开发和验证机器学习模型,用于预测冠状病毒发病途径的激活状态.
使用英才途径分析 (IPA) 分析SARS-CoV-2感染数据,以确定相关的途径和细胞类型.

主要方法:

利用人工智能和机器学习对冠状病毒发病路径的图像.
从IPA数据库中分析了超过10万个SARS-CoV-2相关数据集.
在Python 3.11中开发了一个预测模型,使用来自受感染的人类诱导多能干细胞 (iPSC) 和肺腺癌 (LUAD) 细胞的途径图像.

主要成果:

发现冠状病毒发病途径在感染SARS-CoV-2的iPSC衍生细胞和LUAD细胞中被激活.
机器学习模型已经成功地开发出来,可以预测路径激活状态.
确定了27个相关分析,包括涉及iPSC衍生细胞和LUAD的分析.

结论:

开发的冠状病毒发病路径激活状态的预测模型显示了有助于识别SARS-CoV-2感染的治疗方法的潜力.
这种人工智能驱动的方法为分析复杂的病毒病原路径提供了一种新的方法.

关键词:

人工智能的人工智能是人工智能.这是一种冠状病毒感染.冠状病毒冠状病毒的新冠病毒.机器学习是机器学习.分子网络是分子网络.分子路径图像分子路径图像网络分析网络分析路径分析路径分析预测模型预测模型

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