预测基于图形自编码器和感应矩阵完成的微生物疾病关联,具有多重相似性的融合.
Kai Shi1,2, Kai Huang1, Lin Li1
1College of Computer Science and Engineering, Guilin University of Technology, Guilin, China.
Frontiers in microbiology
|October 2, 2024
概括
本研究介绍了GIMMDA,这是一个深度学习框架,用于识别微生物与疾病的关联. 吉姆达在预测这些关键相互作用方面表现出很高的准确性,有助于理解人类健康和疾病.
科学领域:
- 微生物学 微生物学
- 计算生物学 计算生物学
- 基因组学就是基因组学.
背景情况:
- 微生物显著影响人类健康和疾病.
- 识别微生物与疾病的相互作用是对病原体的洞察力,诊断和治疗的关键.
- 目前用于微生物疾病关联查的计算方法由于数据不一致和未充分利用的先前信息而缺乏准确性和效率.
研究的目的:
- 开发一个改进的深度学习框架,GIMMDA,用于识别潜在的微生物疾病关联.
- 提高预测微生物与疾病关系的准确性和效率.
- 为了利用图形自编码器和感应矩阵完成,实现强大的关联预测.
主要方法:
- 提出了GIMMDA,这是一个使用图形自编码器和感应矩阵完成的深度学习框架.
- 采用了跨微生物和疾病空间的联合培训策略,以产生新的表征.
- 实施了相似性融合策略,以在端到端框架内提高预测性能.
主要成果:
- 在三个数据集 (HMDAD,Disbiome,multiMDA) 上,GIMMDA 实现了与最先进的方法相比具有竞争力的性能.
- 在接收器操作特征曲线 (AUC) 下获得的高面积得分:0.9735,0.9156和0.9396.
- 关于喘和肥胖的案例研究验证了该模型的有效性和可靠性,证实了相似融合的好处.
结论:
- 吉姆达模型在预测微生物与疾病的关联方面表现出强大的能力.
- 预计该框架将有助于识别潜在的微生物相关疾病.
- 进一步开发可能会提高复杂的微生物与疾病相互作用的预测.
更多相关视频
08:51Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts
Published on: September 20, 2024
1.2K
09:47Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
Published on: December 15, 2023
979
相关概念视频
01:18Steps in Outbreak Investigation
108
In the ever-evolving field of public health, statistical analysis serves as a cornerstone for understanding and managing disease outbreaks. By leveraging various statistical tools, health professionals can predict potential outbreaks, analyze ongoing situations, and devise effective responses to mitigate impact. For that to happen, there are a few possible stages of the analysis:
108
01:11Genome-wide Association Studies-GWAS
13.2K
Genome-wide association studies or GWAS are used to identify whether common SNPs are associated with certain diseases. Suppose specific SNPs are more frequently observed in individuals with a particular disease than those without the disease. In that case, those SNPs are said to be associated with the disease. Chi-square analysis is performed to check the probability of the allele likely to be associated with the disease.
GWAS does not require the identification of the target gene involved in...
GWAS does not require the identification of the target gene involved in...
13.2K