ProteoNet: A CNN-based framework for analyzing proteomics MS-RGB images
Jinze Huang,
Yimin Li,
Bo Meng,
Yong Zhang,
Yaoguang Wei,
Xinhua Dai,
Dong An,
Yang Zhao,
Xiang Fang
Affiliations
Jinze Huang
Technology Innovation Center of Mass Spectrometry for State Market Regulation, Center for Advanced Measurement Science, National Institute of Metrology, Beijing 100029, China
Yimin Li
College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China
Bo Meng
Technology Innovation Center of Mass Spectrometry for State Market Regulation, Center for Advanced Measurement Science, National Institute of Metrology, Beijing 100029, China
Yong Zhang
Institutes for Systems Genetics, West China Hospital, Sichuan University, Chengdu 610041, China
Yaoguang Wei
College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China
Xinhua Dai
Technology Innovation Center of Mass Spectrometry for State Market Regulation, Center for Advanced Measurement Science, National Institute of Metrology, Beijing 100029, China; Corresponding author
Dong An
College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China; Corresponding author
Yang Zhao
Technology Innovation Center of Mass Spectrometry for State Market Regulation, Center for Advanced Measurement Science, National Institute of Metrology, Beijing 100029, China; College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China; Corresponding author
Xiang Fang
Technology Innovation Center of Mass Spectrometry for State Market Regulation, Center for Advanced Measurement Science, National Institute of Metrology, Beijing 100029, China; Corresponding author
Summary: Proteomics is crucial in clinical research, yet the clinical application of proteomic data remains challenging. Transforming proteomic mass spectrometry (MS) data into red, green, and blue color (MS-RGB) image formats and applying deep learning (DL) techniques has shown great potential to enhance analysis efficiency. However, current DL models often fail to extract subtle, crucial features from MS-RGB data. To address this, we developed ProteoNet, a deep learning framework that refines MS-RGB data analysis. ProteoNet incorporates semantic partitioning, adaptive average pooling, and weighted factors into the Convolutional Neural Network (CNN) model, thus enhancing data analysis accuracy. Our experiments with proteomics data from urine, blood, and tissue samples related to liver, kidney, and thyroid diseases demonstrate that ProteoNet outperforms existing models in accuracy. ProteoNet also provides a direct conversion method for MS-RGB data, enabling a seamless workflow. Moreover, its compatibility with various CNN architectures, including lightweight models like MobileNetV2, underscores its scalability and clinical potential.
