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In the latest episode of The Genetics Podcast, host and Sano Genetics CEO Patrick Short explores the world of aging research with Dr. Austin Argentieri. Dr. Argentieri is a research fellow at Harvard and the Broad Institute, working in the Analytical and Translational Genetics Unit at Massachusetts General Hospital. With a background that includes a PhD and postdoc from the Big Data Institute at the University of Oxford, Dr. Argentieri focuses on large-scale analyses to understand the genetic, biological, and environmental determinants of human aging and aging-related diseases.
While AI can be incredibly helpful in interpreting complex genomic data and predicting patient outcomes, using it within healthcare is not without challenges and raises important ethical considerations as well. Here, we'll outline some of these important issues:
In the latest episode of The Genetics Podcast, Sano CEO Patrick Short sat down with Daniel O’Connor, an expert in regulatory policy for innovative medicines, particularly those focused on rare diseases. Daniel, who spent nearly 20 years at the MHRA (Medicines and Healthcare products Regulatory Agency) and recently joined the ABPI (Association of the British Pharmaceutical Industry), shared his extensive experience and insights into the regulatory landscape of rare disease drug development.
At Sano, we are constantly exploring new ways to use our technology expertise to make a meaningful impact on healthcare. That’s why we’re excited about our partnership with the Lupus Research Alliance (LRA) on a profoundly important project: the Lupus Nexus initiative. This collaboration represents a significant step forward in our efforts to contribute to critical advancements in the field of lupus research.
The progress in genome sequencing has catalyzed a significant transformation in the field of digital biology. Genomics programs across the world are gaining momentum as the cost of high-throughput, next-generation sequencing has dropped dramatically over the past decade. Now, whole genome sequencing is becoming a fundamental step in clinical workflows and drug discovery, especially for critical-care patients with rare diseases and in population-scale genetics research. However, traditional methods for analyzing genomic data are facing challenges in coping with the explosion of bioinformatics data.
In the most recent episode of The Genetics Podcast, Sano CEO Dr. Patrick Short explored the latest discoveries in genetics and precision medicine with returning guest Dr. Veera Rajagopal. Dr. Veera, a scientist at Regeneron and quarterly guest on our podcast, provided insights into recent influential studies reshaping our understanding of genetic disorders and their implications for medical treatment.
Predicting how a patient will react to medication or treatment involves understanding many factors, including their genetic makeup. Each patient's genome can hold clues about how they might respond to certain drugs, their risk of adverse drug reactions, or their susceptibility to particular diseases. However, the sheer volume and complexity of genomic data make it challenging for traditional analysis methods to efficiently process and interpret this information.
In the latest episode of The Genetics Podcast, Sano CEO Patrick Short sat down with Jakob Steinfeldt, co-founder and Chief Scientific Officer at Pheiron. Jakob shared his journey from academia to entrepreneurship and the innovative work Pheiron is doing in disease prediction and drug development.
In a recent webinar, Hayley Holt, Senior Programme Manager at Sano Genetics, provided an insightful discussion on Sano's innovative approach to patient finding, starting with the development of a patient finding protocol.
AI's significance in genomics lies in its ability to uncover hidden patterns, provide diagnostic insights, and enhance our understanding of genetic information. The reason AI can be so helpful in genetics is that the complexity and sheer volume of genomic data poses significant challenges to traditional methods, which struggle to efficiently analyse and interpret this information. AI addresses this hurdle by offering computational tools capable of handling, extracting, and deciphering valuable insights from a huge amount of data.