Calibration method and system for different queues in metabolism analysis based on convolutional neural network
A technology of convolutional neural network and calibration method, which is applied in neural learning methods, biological neural network models, biological systems, etc.
Pending Publication Date: 2022-01-07
上海中科新生命生物科技有限公司
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[0007] The purpose of the present invention is to provide a method and system for calibrating different queues in metabolic analysis based on convolutional neural network, to at least solve the problem of how to reduce the systematic deviation caused by different batches in metabolic analysis
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[0049] The method and system for calibrating different queues in the convolutional neural network-based metabolic analysis proposed by the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be noted that all the drawings are in a very simplified form and use imprecise scales, and are only used to facilitate and clearly assist the purpose of illustrating the embodiments of the present invention. In addition, the structures shown in the drawings are often a part of the actual structures. In particular, each drawing needs to display different emphases, and sometimes uses different scales.
[0050] It should be noted that "first", "second", etc. in the specification, claims and description of the drawings of the present invention are used to distinguish similar objects in order to describe the embodiments of the present invention, and are not used to describe specific It is to be understood t...
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The invention provides a calibration method and system for different queues in metabolism analysis based on a convolutional neural network, and the method comprises the steps: obtaining a plurality of different batches of sample sets, wherein each batch of sample set comprises at least one sample; carrying out feature extraction on all samples, and carrying out three-axis association; carrying out dimension reduction processing on the extracted feature data; classifying and predicting the feature data subjected to dimension reduction to obtain feature data which does not have classification accuracy and has the minimum batch effect; and carrying out integrated dimension raising deep network learning on the feature data to obtain an optimal coding calibration result. Feature construction and deep network learning are carried out through the convolutional neural network, so that information extraction and analysis of front and back samples are effectively carried out, classification and regression prediction are carried out on a coding layer through the convolutional neural network, the features of the batch effect are minimized, and an optimal classification result is obtained. The problem of how to reduce system deviation caused by different batches in metabolism analysis is solved.
Description
technical field [0001] The invention relates to the technical field of metabolomics, in particular to a method and system for calibrating different queues in metabolic analysis based on convolutional neural networks. Background technique [0002] Metabonomics (metabonomics / metabolomics) is a research method that imitates the research ideas of genomics and proteomics, conducts quantitative analysis of all metabolites in organisms, and searches for the relative relationship between metabolites and physiological and pathological changes. Most of its research objects are small molecular substances with a relative molecular mass of less than 1000. Advanced analysis and detection technology combined with computational analysis methods such as pattern recognition and expert system are the basic methods of metabolomics research. [0003] Metabolomics One of the strategies to achieve comprehensive coverage during analysis is untargeted metabolomics. Untargeted metabolomics is drive...
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Login to View More IPC IPC(8): G16B5/00G16B40/00G06N3/04G06N3/08
CPCG16B5/00G16B40/00G06N3/08G06N3/045
Inventor 阮宏强张鹏张惠萍
Owner 上海中科新生命生物科技有限公司