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Coming from the field of digital pathology, Joachim Schmid has a unique perspective on the evolution of multi-omics. Schmid was recently appointed as Vice President of Multiomics Data Solutions at Illumina.In this episode, Theral dives into the burgeoning field of multi-omics—integrating data from various omic layers such as genomics, proteomics, and transcriptomics—and the massive data challenges that come with it. Schmid discusses how sequencing costs are plummeting, leading to an explosion in multi-omics applications. Yet, with all the data being generated, biologists often lack the computational background to make sense of it. Schmid highlights Illumina's acquisition of Partek, a company that has been developing multi-omics data analysis tools for over 30 years, and he emphasizes that empowering researchers with accessible analysis tools is crucial to advancing the field.AI is poised to play a key role in moving multi-omics forward. Schmid discusses both the opportunities and limitations of AI, noting that while AI can streamline data processing, there are still areas for improvement, particularly in developing more intuitive and user-friendly tools for biologists. He shares some exciting applications of multi-omics in personalized medicine and cancer research.
By Theral Timpson4.6
3434 ratings
Coming from the field of digital pathology, Joachim Schmid has a unique perspective on the evolution of multi-omics. Schmid was recently appointed as Vice President of Multiomics Data Solutions at Illumina.In this episode, Theral dives into the burgeoning field of multi-omics—integrating data from various omic layers such as genomics, proteomics, and transcriptomics—and the massive data challenges that come with it. Schmid discusses how sequencing costs are plummeting, leading to an explosion in multi-omics applications. Yet, with all the data being generated, biologists often lack the computational background to make sense of it. Schmid highlights Illumina's acquisition of Partek, a company that has been developing multi-omics data analysis tools for over 30 years, and he emphasizes that empowering researchers with accessible analysis tools is crucial to advancing the field.AI is poised to play a key role in moving multi-omics forward. Schmid discusses both the opportunities and limitations of AI, noting that while AI can streamline data processing, there are still areas for improvement, particularly in developing more intuitive and user-friendly tools for biologists. He shares some exciting applications of multi-omics in personalized medicine and cancer research.

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