Forbes' richest doctor, Chen Shaoxiong, acquired gene sequencing Genos
Recently, NantOmics, a leader in molecular diagnostics and a subsidiary of NantWorks, announced the acquisition of Genos to accelerate the development of the company's precision medical platform.
In January 2013, Forbes was named the richest doctor in history, and Chen Shaoxiong established the biotechnology company NantOmics to develop anticancer drugs. NantOmics provides molecular diagnostic services, the first to explore the differences between cancer-driven genomes and proteomics, by analyzing the normal cells and tumor cells of the same patient, and determining what happens to the process of DNA translation into proteins after taking the drug.
NantOmics has a highly scalable cloud platform-based approach that can store and process thousands of genomes per day, and can realistically calculate genomic differences, using quantitative multiplex protein expression analysis to perform related proteomes Pathway analysis.
In 2016, Genos was separated by Complete Genomics, whose mission is to break down barriers and accelerate disease research and treatment. Since its inception, Genos has quickly become a leader in the second generation of sequencing. The company sells FDA-approved home test kits for $499. Users only need to provide a sample of saliva to Genos, and Genos will then provide a network visualization model of the gene for users to explore their genetic code.
With the goal of making sequencing easier, Genos gives participants more than 50 times more data than data from current popular sequencing projects and gives participants the opportunity to join a groundbreaking research project. More importantly, participants have the discretion to make personal data.
This time, NantOmics plans to integrate Genos' technology, data visualization and research platforms into its pan-omic analysis tools to further strengthen its position in the precision medical services industry, including genome-wide sequencing, transcriptome, and proteomics. And quantitative proteomics.
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