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通过综合损失策略,优化基于变压器的预测人类微生物疾病的相关性

Rong Zhu¹, Yong Wang², Junliang Shang¹

¹School of Computer Science, Qufu Normal University, Rizhao, Shandong, China.

PeerJ. Computer science

|September 24, 2025

概括

这项研究引入了一种新的计算框架,HGNNTMDA,用于预测微生物与疾病的关联. 该模型显著提高了准确性和稳定性,为了解复杂疾病和开发新疗法提供了更有效的方法.

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:

微生物显著影响人类疾病,影响其发展和治疗.
鉴定微生物与疾病联系的传统实验方法耗时且资源密集.
计算方法提供了一个实际的替代方案,但在准确性和数据处理方面面临挑战.

研究的目的:

开发一种新的计算框架,HGNNTMDA,用于准确预测微生物与疾病的关联.
通过探索微生物的作用来提高对疾病机制的理解.
为识别潜在的诊断和治疗目标提供更有效的工具.

主要方法:

提出了一个超图形神经网络与转换器的微生物疾病协会 (HGNNTMDA) 框架.
综合微生物疾病关联数据与相似性特征,使用KNN和K-means集群构建图形和超图形.
采用单独的HGNN,注意力机制和用于特征提取和上下文依赖性捕获的变压器模块.
使用混合损失策略 (对比和休伯损失) 来进行模型优化.

主要成果:

HGNNTMDA实现了高预测性能,HMDAD的AUC为0.9976,Disbiome的AUC为0.9423 .
该模型在预测微生物与疾病的关联方面表现优于现有的六种最先进的方法.
案例研究表明,该框架在识别新型微生物与疾病的联系方面具有实际实用性.

结论:

HGNNTMDA提供了一种强大而准确的计算方法,用于预测微生物与疾病的关联.
该框架解决了现有方法的局限性,特别是在处理杂或稀疏数据方面.
这项工作有助于更深入地了解微生物组在人类健康和疾病中的作用,为新的治疗策略铺平了道路.

关键词:

相反的学习学习.在HGNN中,HGNN是指高海拔的.哈伯的损失哈伯的损失微生物疾病关联预测预测.变压器变压器变压器

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