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Mining the Unlimited Value of Biomedical Data
Empower Precise Decision-Making Medical Solutions

Big Data Analysis Center

01Medical Big Data Processing Services, Accelerating The Development Of Precision Medicine

Zhongyuan Biotech has brought together senior experts in big data mining and biomedical information analysis at home and abroad, and actively promoted the use of more data in artificial intelligence and big data tools (AI AND BIG DATA), extracting meaningful laws from it, and solving Clinical healthcare related issues and assist in making optimal decisions. The data analysis team provides a series of one-stop innovative solutions, algorithms and technical support, and has continued to cooperate with many top domestic hospitals. Based on the data mining results provided, several SCI articles have been published in international journals. This has further consolidated Zhongyuan Biotech 's competitiveness in the industry.

02Cross-discipliner Data Mining Strength—metabolomics Combined With Big Data Mining Technology, Found That It Is In Line With Biological And Clinical Significance

In 2019, Zhongyuan Biotech subsidiary joined hands with the Chinese National Health Commission to jointly build the "Key Laboratory of Genetic Metabolism Research", and has a group of talents with multi-disciplinary backgrounds, such as cell biology, genomics and data analysis as technical support. At the same time, the company and Zhongguancun big data industry alliance jointly established the " Zhong Guan Cun Biological and Medical Big Data Center" in 2020, Dr. Chen Xianyang, CTO of the group, as a partner of WATERSTM, has mastered the world's leading mass spectrometry development technology of laboratory departments, and has made great achievements in the intelligent diagnosis of metabolic diseases. With these advantages, we have worked with several leading hospitals in China to identify biomarkers of important medical significance. In his research with the First Affiliated Hospital of Tsinghua University, through metabolomics and artificial intelligence integration, they obtained breast cancer diagnostic markers, jointly published international journals (DOI:HTTPS://DOI.ORG/10.1007/S10529-017-2417-Z), and opened up a new idea for early screening of breast cancer. For more information, please follow Zhongyuan biological R & D platform.

03MS And Clinical Data Algorithm Development—provide Intelligent Decision Support And Overall Solutions For Scientific Research And Clinical Workers

MS and clinical data algorithm development—An intelligent decision support and overall solutions for scientific research and clinical workers Combined with the DAP data function box, multiple databases, such as LIPID MAPS and HMDB database can be used to classify the identified compounds; At the same time, it also has a built-in classification algorithm, which can effectively classify and visualize non-metabolomic data, and use clinical scientific research data to master the most detailed and accurate data situation and possible patterns in the shortest time. According to different data types and structures, the expert team will evaluate and select the correct analysis method, and perform multi-type cross-validation to ensure the accuracy of the analysis process. At the same time, through the principal component dimension reduction of multi-dimensional variables, to achieve unsupervised division of samples. Using the (O)PLS model, the samples are supervised and divided, and the important variables that affect the classification of the samples are identified through the variable importance projection, as the main difference index between different types, and the candidate compound of BIOMARKER. At the current stage, the big data analysis service can construct, evaluate, and compare a variety of different models at the same time, and quickly and accurately obtain the most suitable model for matching biomarkers. The results obtained are also relatively intuitive, which further provides decision support and a full range of overall solutions for scientific research and clinical workers.