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萨尔格姆达:基于稀疏的自编码器和LightGBM的基础上识别人类微生物疾病的关联

Feixiang Wang¹, Huandong Yang², Yan Wu³

¹School of Computer Science, Hunan University of Technology, Zhuzhou, China.

Frontiers in microbiology

|July 7, 2023

概括

一种新的计算方法SAELGMDA准确地预测了微生物与疾病的关联 (MDA),确定了微生物与结直肠癌和炎症性肠病等疾病之间的潜在联系.

科学领域:

计算生物学是一种计算生物学.
微生物组研究的研究.
疾病的病原发生疾病的病原发生

背景情况:

识别微生物疾病关联 (MDAs) 对于理解疾病机制和开发疗法至关重要.
传统的MDA检测实验方法是昂贵的,耗时的,劳动密集的.

研究的目的:

开发一种高效的计算方法,SAELGMDA,用于预测潜在的微生物疾病关联.
为了克服实验MDA检测的局限性.

主要方法:

SAELGMDA集成了功能和高斯相互作用配置的内核相似性,用于微生物和疾病相似性计算.
特性向量是通过结合相似度矩阵来创建的,并使用Sparse自动编码器映射到一个低维空间.
光渐变增强机用于分类未知的微生物疾病对.

主要成果:

与HMDAD和Disbiome数据库中的四种最先进的MDA预测方法相比,SAELGMDA表现优越.
该方法在交叉验证研究中实现了高精度,马修斯相关系数,AUC和AUPR值.
SAELGMDA确定了潜在的关联,包括*Clostridium coccoides*与结直肠癌和Sphingomonadaceae与炎症性肠病.

结论:

该SAELGMDA方法提供了一个有前途的计算方法,用于发现新的微生物疾病关联.
这种工具可以帮助识别潜在的微生物生物标记物和各种疾病的治疗点.

关键词:

轻GBMM 轻GBMM 的时间减小尺寸的缩小方式功能表示的特征表示.微生物疾病协会微生物疾病协会稀有的自动编码器.

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