NNSFMDA:轻型变压器模型与有限的核规范最小化用于微生物-药物协会预测
Shuyuan Yang1, Xin Liu2, Yiming Chen2
1School of Mathematics, Changsha University, Changsha 410022, China; Big Data Innovation and Entrepreneurship Education Center of Hunan Province, Changsha University, Changsha 410022, China.
Journal of molecular biology
|March 26, 2025
概括
这项研究介绍了NNSFMDA,这是一种用于预测微生物与药物相关性的新型模型. NNSFMDA有效地识别了潜在的药物-微生物联系,帮助药物发现和临床应用.
科学领域:
- 微生物学和药理学 微生物学和药理学
- 计算生物学和生物信息学
- 药物发现和开发 药物发现和开发
背景情况:
- 识别微生物与药物之间的关联对于药物发现和临床治疗至关重要.
- 像图形神经网络 (GNN) 这样的现有方法面临过度平滑,过度压缩和解释性差等挑战.
- 使用低级近似的数学方法提供可解释性,但可能受到局部最佳值的限制.
研究的目的:
- 开发一种新的预测模型,NNSFMDA,它结合了数学和机器学习方法的优势,用于推断微生物药物协会.
- 克服现有方法的局限性,包括解释性问题和对局部最佳的敏感性.
主要方法:
- 构建了一个异质的微生物药物网络,集成了各种相似度指标.
- 制定了预测问题作为矩阵完成任务,通过最小化边界核规范来代地近似矩阵.
- 在完成的矩阵上使用简化的变压器架构来预测微生物-药物对协会.
主要成果:
- NNSFMDA实现了0.98的高曲线下面积 (AUC) 值,超过了现有的最先进的方法.
- 废弃实验和模块化分析验证了该模型的卓越性能.
- 案例研究证实了该模型能够识别有效的微生物药物关联的能力.
结论:
- NNSFMDA证明了在准确预测潜在微生物与药物相关性方面具有重大潜力.
- 该模型的混合方法在微生物药物相互作用预测中提供了更好的准确性和可靠性.
- NNSFMDA为推进药物发现和个性化医学提供了一个有前途的工具.
相关概念视频
01:23Mechanistic Models: Compartment Models in Individual and Population Analysis
21
Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
21
01:29Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving
35
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
35
01:26Pharmacokinetic Models: Comparison and Selection Criterion
31
Physiological and compartmental models are valuable tools used in studying biological systems. These models rely on differential equations to maintain mass balance within the system, ensuring an accurate representation of the dynamic processes at play.
Physiological models take a detailed approach by considering specific molecular processes. They can predict drug distribution, metabolism, and elimination changes, providing a comprehensive understanding of how drugs interact with the body.
Physiological models take a detailed approach by considering specific molecular processes. They can predict drug distribution, metabolism, and elimination changes, providing a comprehensive understanding of how drugs interact with the body.
31
01:06Model Approaches for Pharmacokinetic Data: Distributed Parameter Models
54
Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
54
01:13Physiological Pharmacokinetic Models: Assumption with Protein Binding
29
Physiological models with protein binding in pharmacokinetics offer a sophisticated approach to understanding drug disposition. These models consider drug-protein interactions, enabling them to effectively predict drug concentrations in different organs and tissues. This precision aids in accurate drug dosing, providing a significant advantage over conventional models. A key process within these models is equilibration, which ensures that drug concentrations achieve a steady state within the...
29