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Microsoft Teams Up with Taiwan’s AI LAB to Jointly Announce AI Gene Analysis Platform

By Korbin Lan
Published: Jan 02,2019

TAIPEI, Taiwan - Taiwan Microsoft and Taiwan’s Artificial Intelligence Laboratory (AI LAB) have jointly announced the TaiGenomics Artificial Intelligence gene analysis platform. Integrated with Microsoft’s Azure Cloud Computing, the platform conducts gene sequencing, analysis, and diagnosis effectively through the use of AI to assist medical specialists in the rapid prediction of potential diseases caused by genetic variations.

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Taiwan AI LAB Founder Ethan Tu stated that Taiwan has an advantage in the development of precision medical care, and in addition to having a solid foundation in the communications industry, Taiwan’s National Health Insurance Database, medical records, and medical electronic imaging also provide robust databases for genetic testing.

This collaboration with Microsoft utilizes the advantages of Azure to develop TaiGenomics, which analyzes the necessary space and cloud computing resources necessary for the analysis of medical big data. It is anticipated that this collaboration will enable TaiGenomics to develop Taiwan’s strengths and drive a new ecology through integration with the medical treatment ecosystem. Furthermore, there is an opportunity to begin this endeavor in Taiwan and then extend it to the rest of the world.

General Manager of Microsoft Taiwan Sun Jikang stated: AI has always been Microsoft’s goal, and we anticipate that the joint development of the TaiGenomics gene analysis platform with Taiwan’s AI LAB will serve as a connection with other team research services and experts to expand the precision medical ecosystem.

In order to integrate with Azure cloud computing, massive amounts of data are processed for gene detection using AI, which drastically reduces data comparison time and gene testing costs. It also assist medical specialists in the rapid prediction of potential diseases. As for the resources to establish TaiGenomics, they will all be placed in cloud storage in order to solve the problem of massive data needed for genetic testing.

(TR/ Phil Sweeney)

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