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<p>Analysing metabolites in bioliquids through various spectroscopic methods provides valuable insights into the metabolic phenotypes. Deciphering spectral data has greatly benefited from deep-learning methods; however, data-driven solutions often struggle with data dependence on different devices, samples and spectral modalities. Most current task-specific methods have limited generalizability to different spectral analysis problems, including preprocessing, quantification and interpretation. Here, we developed a pretrained foundation model, termed deep-spectral component filtering (DSCF) through a self-supervised approach termed spectral component resolvable learning. By acquiring general spectral knowledge, DSCF achieved state-of-the-art performance for five distinct spectral analysis tasks on 11 datasets. Notably, the general pretraining led to zero-shot spectral denoising and trace-level quantification in complex mixtures. DSCF achieved molecule-level interpretation of surface-enhanced Raman spectra and mapped serum metabolic profiles from nearly 600 individuals for various diseases, including stroke, Alzheimer’s disease and prostate cancer. Overall, the proposed foundation model illustrates promising generalizability for spectral analysis and offers a clear and feasible pathway for general spectral analysis.</p>

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Deep spectral component filtering as a foundation model for spectral analysis demonstrated in metabolic profiling

  • 期刊论文

  • 发表日期：

    2025年5月23日

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  • Bingsen Xue,
  • Xinyuan Bi,
  • Zheyi Dong,
  • Yunzhe Xu,
  • Minghui Liang,
  • Xin Fang,
  • Yizhe Yuan,
  • Ruoxi Wang,
  • Shuyu Liu,
  • Rushi Jiao,
  • Yuze Chen,
  • Weitao Zu,
  • Chengxiang Wang,
  • Jianhao Zhang,
  • Jiang Liu,
  • Qin Zhang,
  • Ye Yuan,
  • Midie Xu,
  • Ya Zhang,
  • Yanfeng Wang,
  • Jian Ye,
  • Cheng Jin

摘要

Analysing metabolites in bioliquids through various spectroscopic methods provides valuable insights into the metabolic phenotypes. Deciphering spectral data has greatly benefited from deep-learning methods; however, data-driven solutions often struggle with data dependence on different devices, samples and spectral modalities. Most current task-specific methods have limited generalizability to different spectral analysis problems, including preprocessing, quantification and interpretation. Here, we developed a pretrained foundation model, termed deep-spectral component filtering (DSCF) through a self-supervised approach termed spectral component resolvable learning. By acquiring general spectral knowledge, DSCF achieved state-of-the-art performance for five distinct spectral analysis tasks on 11 datasets. Notably, the general pretraining led to zero-shot spectral denoising and trace-level quantification in complex mixtures. DSCF achieved molecule-level interpretation of surface-enhanced Raman spectra and mapped serum metabolic profiles from nearly 600 individuals for various diseases, including stroke, Alzheimer’s disease and prostate cancer. Overall, the proposed foundation model illustrates promising generalizability for spectral analysis and offers a clear and feasible pathway for general spectral analysis.