MLFLHMDA:基于多视图隐藏特征学习的预测人类微生物疾病关联.
Ziwei Chen1, Liangzhe Zhang1, Jingyi Li1
1School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing, China.
Frontiers in microbiology
|February 19, 2024
概括
这项研究介绍了MLFLHMDA,这是一种用于预测微生物疾病关联的计算模型. 这种新的方法提高了识别微生物在人类健康和疾病发病过程中的作用的准确性.
科学领域:
- 微生物学 微生物学
- 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:
- 微生物对人类健康至关重要,与疾病相关的不平衡.
- 识别微生物与疾病的关联有助于理解疾病的发病因子.
- 协会发现的传统实验方法昂贵且耗时.
研究的目的:
- 开发一种新的计算模型,MLFLHMDA,用于预测人类微生物与疾病的关联.
- 提高识别潜在的微生物疾病联系的准确性和效率.
- 为研究微生物在人类健康中的作用提供一个有价值的工具.
主要方法:
- 使用多视图潜伏特征学习方法.
- 计算高斯互动概况核相似性和应用权重K最近已知的邻居 (WKNKN) 进行预处理.
- 提取了潜在的特征,将它们投射到一个共同的子空间中,并将图形规范化和L-规范用于可解释性和稀疏性.
主要成果:
- 实现了高性能,AUC值为0.9165 (全球离开一次) 和0.8942+/-0.0041 (5倍交叉验证).
- 通过案例研究,与现有方法相比,证明了更高的预测能力.
- 该模型的源代码和数据是公开可用的.
结论:
- MLFLHMDA有效地预测了人类微生物与疾病的关联.
- 该模型为传统实验方法提供了具有成本效益和准确性的替代方案.
- 这项工作有助于更深入地了解人类微生物组在健康和疾病中的作用.
相关概念视频
01:11Genome-wide Association Studies-GWAS
13.4K
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...
13.4K
01:18Steps in Outbreak Investigation
128
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:
128