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Automated diagnostic analyzers have transformed clinical microbiology by providing rapid and reliable methods for pathogen identification and antibiotic susceptibility testing. Among these systems, the Vitek 2 is widely used because it automates the traditionally labor-intensive processes of microbial identification (ID) and antibiotic susceptibility testing (AST), delivering standardized and timely results that are essential for effective patient care.Microbial Identification with ID CardsThe...

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ZNGEA:ZINB-NMF集成图嵌入自动编码器用于代谢物-疾病关联识别.

Qiao Ning1, Yanpeng Liu2, Shaohang Qiao2

  • 1School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi 214122, China.

Analytical chemistry
|December 8, 2025
PubMed
概括
此摘要是机器生成的。

一个新的深度学习算法,ZNGEA,通过整合零膨胀负二项式 (ZINB) 和非负矩阵因子化 (NMF) 来有效预测代谢物-疾病关联. 这种计算方法超越了现有的方法,有助于生物医学研究.

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科学领域:

  • 计算生物学是一种计算生物学.
  • 生物信息学是一种生物信息学.
  • 系统生物学 系统生物学

背景情况:

  • 代谢对生命至关重要,而改变的代谢物与疾病有关.
  • 鉴定代谢物与疾病联系的传统实验方法耗时且劳动密集.
  • 需要计算方法来有效识别代谢物与疾病的关联.

研究的目的:

  • 开发一种新的深度学习算法,ZNGEA,用于预测代谢物和疾病之间的潜在关联.
  • 克服传统实验方法在识别代谢物-疾病联系方面的局限性.

主要方法:

  • ZNGEA集成了零膨胀负二项式 (ZINB) 分布和非负矩阵因数分解 (NMF).
  • 使用非线性方法结合多种疾病和代谢物相似性网络.
  • 应用NMF和基于ZINB的图形卷积自编码器用于特征提取.
  • 使用双线解码器来训练模型.

主要成果:

  • 在5倍的交叉验证中,ZNGEA实现了0.9859的曲线下的面积 (AUC) 和0.9820的精度回忆曲线下的面积 (AUPR).
  • 性能超过了现有的方法.
  • 案例研究验证了大多数新发现的代谢物与疾病的联系,证实了ZNGEA的可靠性.

结论:

  • ZNGEA是一种可靠和高效的计算工具,用于预测代谢物与疾病的关联.
  • 该方法为生物医学研究提供了宝贵的资源,用于探索代谢物和疾病之间的潜在联系.
  • 源代码和数据集是公开可用的,用于可复制性和进一步研究.