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<p>Mass spectrometry, known for its high sensitivity, selectivity, rich structural information, and rapid analysis capabilities, is widely used in disease diagnosis and bioanalysis. Despite progress in classification methods/tools for data collection in the past decade, problems such as complex data processing, weak model characterization, and large interbatch differences persist. To address these problems, we present MS-DREDFeaMiC, a deep neural network framework for disease diagnosis and bioanalysis via mass spectrometry data that enables end-to-end training. The trained MS-DREDFeaMiC can integrate mixed features, reduce interbatch differences, and enhance feature distinctions among categories. To demonstrate its wide applicability, ten comparative experiments were conducted with seven public datasets and one self-constructed dataset, and MS-DREDFeaMiC yielded state-of-the-art results. MS-DREDFeaMiC achieved average accuracies that were 6.6% and 6.3% higher than those of Transformer and Mamba, respectively. We anticipate that MS-DREDFeaMiC can be directly applied to routine disease diagnosis and that any mass spectrometry-based classification studies can benefit from such an end-to-end trained model.</p>

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An end-to-end mass spectrometry data classification model with a unified architecture

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年5月30日

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  • Yinchu Wang,
  • Wei Zhang,
  • Lin Guo,
  • Fengyi Zhang,
  • Zilong Liu,
  • Xingchuang Xiong,
  • Xiang Fang

摘要

Mass spectrometry, known for its high sensitivity, selectivity, rich structural information, and rapid analysis capabilities, is widely used in disease diagnosis and bioanalysis. Despite progress in classification methods/tools for data collection in the past decade, problems such as complex data processing, weak model characterization, and large interbatch differences persist. To address these problems, we present MS-DREDFeaMiC, a deep neural network framework for disease diagnosis and bioanalysis via mass spectrometry data that enables end-to-end training. The trained MS-DREDFeaMiC can integrate mixed features, reduce interbatch differences, and enhance feature distinctions among categories. To demonstrate its wide applicability, ten comparative experiments were conducted with seven public datasets and one self-constructed dataset, and MS-DREDFeaMiC yielded state-of-the-art results. MS-DREDFeaMiC achieved average accuracies that were 6.6% and 6.3% higher than those of Transformer and Mamba, respectively. We anticipate that MS-DREDFeaMiC can be directly applied to routine disease diagnosis and that any mass spectrometry-based classification studies can benefit from such an end-to-end trained model.