相关实验视频

Updated: May 17, 2025

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports

Published on: October 13, 2023

GMAMDA:基于自适应硬度负采样和自适应图多重卷积预测代谢物-疾病关联

Binglu Hu¹, Ying Su^2,3, Xuecong Tian²

¹College of Software, Xinjiang University, Urumqi 830046, Xinjiang, China.

Journal of chemical information and modeling

|May 15, 2025

概括

这项研究介绍了GMAMDA,这是一个通过整合图形卷积和自适应负样采集来预测代谢物-疾病关联的新型模型. 为了更好的诊断和治疗,GMAMDA提高了识别疾病相关代谢物的准确性.

科学领域:

生物化学和生物信息学
计算生物学计算生物学
医疗信息学医疗信息学

背景情况:

代谢物度与疾病的发展和进展有关.
准确预测代谢物与疾病的关联对于早期诊断和治疗至关重要.
现有的预测模型往往无法整合节点特征并有效考虑图形结构.

研究的目的:

开发一种新的模型,GMAMDA,用于增强代谢物-疾病关联预测.
解决现有方法在特征集成和负采样方面的局限性.
提高预测潜在代谢物与疾病联系的稳定性和准确性.

主要方法:

使用多个来源的代谢物和疾病相似性信息构建多个异质图.
采用自适应图的多重卷积来生成来自各种邻区的丰富节点表示.
使用PCA的自适应硬度负采样来在培训期间选择高信息负样本.

主要成果:

在最先进的方法中,GMAMDA表现出了优越的性能.
获得了高评估指标:AUC (0.9962),AUPR (0.9967) 和准确性 (0.9733).
对阿尔茨海默氏症和脏疾病的案例研究证实了GMAMDA在识别代谢物标记物的临床实用性.

结论:

对于代谢物与疾病的关联预测,GMAMDA提供了一种强大而准确的方法.
该模型的新策略增强了预测稳定性和临床相关性.
通过生物标志物发现,GMAMDA具有通过生物标志物发现来推进精准医学的巨大潜力.

更多相关视频

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

相关概念视频

Genome-wide Association Studies-GWAS

Genome-wide Association Studies-GWAS

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...

Kaplan-Meier Approach

Kaplan-Meier Approach

The Kaplan-Meier estimator is a non-parametric method used to estimate the survival function from time-to-event data. In medical research, it is frequently employed to measure the proportion of patients surviving for a certain period after treatment. This estimator is fundamental in analyzing time-to-event data, making it indispensable in clinical trials, epidemiological studies, and reliability engineering. By estimating survival probabilities, researchers can evaluate treatment effectiveness,...

Fundamental Mathematical Principles in Pharmacokinetics: Calculus and Graphs

Fundamental Mathematical Principles in Pharmacokinetics: Calculus and Graphs

The fundamental mathematical principles, such as calculus and graphs, play crucial roles in analyzing drug movement and determining pharmacokinetic parameters. Differential calculus examines rates of change and helps to determine the dissolution rate of drugs in biofluids, as well as how drug concentrations change over time. For instance, it can help calculate the rate of elimination of a drug from the body based on its concentration-time profile.
On the other hand, integral calculus focuses on...