使用DERGA对COVID-19患者结果的遗传证明,DERGA是一种新的数据集精制贪算法
Panagiotis G Asteris1, Amir H Gandomi2,3, Danial J Armaghani4
1Computational Mechanics Laboratory, School of Pedagogical and Technological Education, Athens, Greece.
Journal of cellular and molecular medicine
|February 10, 2024
概括
预测COVID-19的严重程度至关重要. 这项研究使用人工智能识别了关键的补充基因变异,在预测患者结果和住院需求方面达到97%的准确性.
科学领域:
- 免疫学 免疫学 免疫学
- 遗传学 遗传学 是一个
- 计算生物学 计算生物学
背景情况:
- 补充抑制在COVID-19等疾病中显示出治疗潜力.
- 准确预测疾病严重程度对于患者管理至关重要.
- 识别影响补充活性的遗传因素对于个性化医学至关重要.
研究的目的:
- 为了确定与COVID-19结果相关的关键补充遗传变异.
- 开发一种基于人工智能的疾病严重程度预测工具.
- 确定基因变异的最佳模式,以准确预测结果.
主要方法:
- 从204名住院COVID-19患者的遗传数据分析.
- 利用基于人工智能 (AI) 的算法来预测结果 (ICU与非ICU入院).
- 采用阿尔法指数来识别预测性遗传变异和DERGA算法来确定模式.
主要成果:
- 通过使用alpha-index识别了30种高度预测的补充基因变异.
- 德加算法在预测COVID-19疾病结果方面实现了97%的准确性.
- 总共检测到977种变异,个体变异范围从40到161个患者.
结论:
- 阿尔法指数是有效的排名众多的遗传变异.
- 用人工智能驱动的对补体遗传变异的分析能够非常准确地预测疾病的结果.
- 这种方法促进了针对个性化COVID-19管理的预测工具的开发.
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