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    Home > Biochemistry News > Biotechnology News > Nature Protocols reports a metabolomics data processing method based on machine learning and parallel computing

    Nature Protocols reports a metabolomics data processing method based on machine learning and parallel computing

    • Last Update: 2022-01-07
    • Source: Internet
    • Author: User
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    Metabolomics is a discipline that simultaneously conducts qualitative and quantitative analysis of all metabolites of a certain organism or cell in a specific physiological period.
    It is widely used to reveal the relationship between small molecules and physiological and pathological effects
    .
    Because of its unique advantages that are closest to the phenotype, it is highly compatible with the goals of researchers in the field of new drug research and development
    .
    Currently, metabolomics has been used in various stages of drug development, including drug target recognition, lead discovery, analysis of drug metabolism, drug research and drug response
    .
    Based on the high cost performance of metabolomics, it has been given high hopes by researchers, and it is expected to significantly accelerate the process of new drug development
    .
    However, the field of metabolomics is still facing serious signal processing and data analysis problems, which pose a huge challenge to its application
    .
    In order to effectively eliminate the undesirable signal fluctuations introduced by the environment, instruments and biological factors, it is necessary to develop a new method for system optimization of metabolomics signal processing, and tailor the optimal data analysis strategy for different omics research
    .

    Recently, the team of Professor Feng Zhu from the School of Pharmacy of Zhejiang University and the Institute of Intelligent Innovative Medicines and the Alibaba - Zhejiang University Future Digital Healthcare Joint Research Center published a research paper entitled " Optimization of metabolomic data processing using NOREVA" in " Nature Protocols " .
    This paper reports a set of methods for optimizing omics signal processing strategies based on machine learning and parallel computing .
    This method scans the existing massive signal processing flow on a large scale, and can quickly optimize the best-performing omics data processing flow based on the raw metabolomics data given by the user .
    This method realizes the data processing of common "time series" and "multi-class" metabolomics problems in the field of new drug development, and is important for drug target discovery, drug metabolism, drug response, and pathological mechanism research of disease occurrence and development.
    Value .




    In order to solve the problem of computing resource bottlenecks faced in the process of large-scale scanning of massive signal processing procedures, this research introduces the parallel computing architecture into metabolomics data processing for the first time
    .
    Tests in this work show that compared to serial computing, the parallel computing integrated by this method increases the operating efficiency by more than 10 times on a personal computer alone .
    Currently, it is advancing the deployment and operation of the Alibaba Cloud platform and external services.

    .

    Doctoral students Fu Jianbo and Zhang Ying from the School of Pharmacy of Zhejiang University are the co-first authors of the article, and Professor Feng Zhu from the School of Pharmacy of Zhejiang University and the Institute of Intelligent Innovative Drugs is the corresponding author
    .
    This research was jointly funded by the Outstanding Youth Project of Zhejiang Natural Science Foundation, the National Talent Project and the Alibaba - Zhejiang University Future Digital Medical Joint Fund Project .

    Original link: https://

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