In this episode, Gabriel and Steve share a conversation with Dr. Ben Sun about multi-omics and the role AI and machine learning are playing in this space for biomarker discovery and disease prediction. Ben shares his insights from being both a clinician and research scientist, and the conversation delves into some really interesting aspects of using AI, including the consideration of the carbon footprint needed to train an algorithm.
In this episode, the hosts have an intriguing discussion with Dr. Ben Sun, Head of Biomarker Genetics at Biogen. Dr. Sun holds an MD/PhD from the University of Cambridge, providing a view and perspective on data from both research and clinical perspectives. The advantage of his unique background is that it provides a balanced perspective on large-scale population data and genomic data sets used to train computational models for predicting and informing clinical treatment. The conversation touches on the fundamental science as well as the integration of in silico, in vitro, and in vivo methods. Ben also shares insights on decision-making is different in research vs. clinical spaces and on the types of variations that can make it challenging to train and confidently use AI in healthcare.
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