相关实验视频
Updated: Jul 20, 2025
05:59
Nasal Wipes for Influenza A Virus Detection and Isolation from Swine
Published on: December 4, 2015
9.5K
用深度学习预测器对猪可能的冠状病毒中间宿主角色的风险评估
Shuyang Jiang1, Sen Zhang2, Xiaoping Kang2
1College of Mathematics, Jilin University, Changchun, Jilin 130012, China.
Viruses
|July 29, 2023
概括
猪冠状病毒 (CoV) 可以感染人类,这表明猪可能是中间宿主. 这项研究使用深度学习来预测CoV宿主适应,识别对人类和其他哺乳动物的潜在传播风险.
科学领域:
- 病毒学 病毒学
- 基因组学就是基因组学.
- 生物信息学是一种生物信息学.
背景情况:
- 猪冠状病毒 (CoV) 构成动物性疾病威胁,Suiformes可能充当中间宿主.
- 了解CoV宿主适应对于评估传播给人类和其他哺乳动物的风险至关重要.
研究的目的:
- 开发卷积神经网络 (CNN) 模型,用于预测猪CoVs的宿主适应.
- 调查Suiformes作为CoV传播中介宿主的潜力.
主要方法:
- 使用二核酸组成表示 (DCR) 的ORF1ab和Spike序列的分解.
- 无监督学习的应用来分析CoV宿主关系.
- 建立和验证基于DCR的CNN模型,用于宿主适应性预测.
- 遗传学分析以验证模型的合理性.
主要成果:
- 无监督学习揭示了不同猪冠状病毒之间的多个宿主适应.
- CNN模型预测了SADS-CoV,PEDV,TGEV,PHEV和PDCoV的特定宿主适应.
- 丁核酸组合表示 (DCR) 被证实为CoVs的代表性基因组特征.
结论:
- 基于DCR的深度学习模型有效地评估了猪对哺乳动物的COV适应性.
- 形可能作为人类和其他哺乳动物CoV的中间宿主.
- 这项研究提供了一种新的方法来评估猪冠状病毒适应性和动物传播传播风险.
相关概念视频
00:57Viral Recombination
23.5K
Cells are sometimes infected by more than one virus at once. When two viruses disassemble to expose their genomes for replication in the same cell, similar regions of their genomes can pair together and exchange sequences in a process called recombination. Alternatively, viruses with segmented genomes can swap segments in a process called reassortment.
23.5K
01:03Prediction Intervals
2.3K
The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.
2.3K