Deep Learning Models for Metabolomics Analysis

用于代谢组学分析的深度学习模型

基本信息

  • 批准号:
    10552395
  • 负责人:
  • 金额:
    $ 21.67万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
  • 财政年份:
    2023
  • 资助国家:
    美国
  • 起止时间:
    2023-04-01 至 2028-03-31
  • 项目状态:
    未结题

项目摘要

PROJECT SUMMARY Untargeted metabolomics using tandem mass spectrometry (MS) have attained substantial success in the discovery of biomarkers and advancing our understanding of cellular metabolism. Despite this success, only a small fraction of measured spectra can currently be annotated (assigned a chemical identity). This bottleneck can be attributed to the limitations of current annotation tools that have not yet exploited advances in deep learning and available data modalities (spectra, peaks, molecules, and fragments). The goal of this application is to advance the interpretation of spectra collected through untargeted metabolomics. We focus on annotating data collected through liquid or gas chromatology followed by MS, or MS/MS, as these three tandem technologies have become dominant technologies. Over the next five years, the plan is to harness deep learning to address three problems: 1) annotation, 2) translation between spectra measured under different instrument settings, and 3) explainable models for annotation, where explainability arises from connecting peaks to their respective molecular fragments. The Hassoun lab has extensive, relevant deep learning experience to effectively tackle these problems. The Lab also has experience in dealing with the nuances of metabolomics datasets. The Lab recently developed a novel deep learning annotation model that achieves 41% and 30% performance improvement over multi-layer neural networks and graph neural networks, respectively. Additionally, our lab has developed an ontology- traversal algorithm that yields correct-by-construction molecular substructures that can be assigned to peaks, thus giving rise to datasets that can be used to train explainable annotation models. The Significance of this research is that it addresses fundamental barriers that hinder developing deep learning annotation models. Our models and datasets will be released on GitHub to benefit biological and biomedical applications and metabolomics research. Because of their expected high accuracy and explainability, the models will expedite the interpretation of experiments, improve our understanding of cellular metabolism, and facilitate data sharing among labs. The innovation lies in maximally learn from data modalities and in creating models that exploit the learned representations. Further, the annotation and translation problems are formulated as a bidirectional mapping between domains, in contrast to current annotation models that assume unimodal mappings. These innovations are necessary to advance metabolomics research and they will open new research horizons in the field of metabolomics.
项目总结 利用串联质谱仪(MS)的非靶向代谢组学在 生物标志物的发现,促进了我们对细胞代谢的理解。尽管取得了这样的成功,但只有 目前可以对一小部分测量光谱进行注释(指定化学标识)。这一瓶颈 这可以归因于当前批注工具的局限性,这些工具还没有利用深度 学习和可用的数据形式(光谱、峰、分子和碎片)。此应用程序的目标是 是推进对通过非靶向代谢组学收集的光谱的解释。我们专注于注释 通过液体或气相色谱学收集的数据,然后是MS,或MS/MS,这三个串联 技术已经成为主导技术。未来五年,我们的计划是利用深度学习 解决三个问题:1)注释,2)在不同仪器下测量的光谱之间的转换 设置,以及3)用于注释的可解释模型,其中可解释性源于将峰与其 各自的分子片段。 哈桑实验室拥有广泛的、相关的深度学习经验,可以有效地解决这些问题。 该实验室在处理代谢组学数据集的细微差别方面也有经验。该实验室最近开发了 一种新的深度学习标注模型,多层性能分别提高了41%和30% 神经网络和图神经网络。此外,我们的实验室还开发了一个本体论- 遍历算法,其产生可被分配给峰的按结构校正的分子子结构, 从而产生可用于训练可解释注释模型的数据集。 这项研究的意义在于,它解决了阻碍深入发展的根本障碍 学习标注模型。我们的模型和数据集将在GitHub上发布,以造福于生物学和 生物医学应用和代谢组学研究。由于其预期的高精确度和可解释性, 这些模型将加快对实验的解释,提高我们对细胞新陈代谢的理解, 并促进实验室之间的数据共享。创新在于最大限度地从数据模式中学习,并在 创建利用学习的表示法的模型。此外,注释和翻译问题还包括 被表示为域之间的双向映射,与当前的注释模型不同 单峰映射。这些创新对于推进代谢组学研究是必要的,它们将开启 代谢组学领域的新研究领域。

项目成果

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Soha Hassoun其他文献

Soha Hassoun的其他文献

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{{ truncateString('Soha Hassoun', 18)}}的其他基金

Using Common Fund Datasets to Illuminate Drug-Microbial Interactions
使用共同基金数据集阐明药物-微生物相互作用
  • 批准号:
    10777339
  • 财政年份:
    2023
  • 资助金额:
    $ 21.67万
  • 项目类别:
Computational Techniques for Advancing Untargeted Metabolomics Analysis
推进非靶向代谢组学分析的计算技术
  • 批准号:
    10022125
  • 财政年份:
    2019
  • 资助金额:
    $ 21.67万
  • 项目类别:
Computational Techniques for Advancing Untargeted Metabolomics Analysis
推进非靶向代谢组学分析的计算技术
  • 批准号:
    10394012
  • 财政年份:
    2019
  • 资助金额:
    $ 21.67万
  • 项目类别:
Computational Techniques for Advancing Untargeted Metabolomics Analysis
推进非靶向代谢组学分析的计算技术
  • 批准号:
    10242075
  • 财政年份:
    2019
  • 资助金额:
    $ 21.67万
  • 项目类别:
Computational Techniques for Advancing Untargeted Metabolomics Analysis
推进非靶向代谢组学分析的计算技术
  • 批准号:
    10480818
  • 财政年份:
    2019
  • 资助金额:
    $ 21.67万
  • 项目类别:

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