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#102, Decoding Human Biology with Single-Cell Insights, with Lindsay Edwards from Relation
Manage episode 441652004 series 3291628
In this episode of the AWS Health Innovation Podcast, host Yin He, Principal Business Development for Healthcare & Life Sciences Startups at AWS, sits down with Lindsay Edwards, CTO and President of Platform at Relation, a biotech company leveraging single-cell multi-omics, machine learning, and clinical insights to develop transformational medicines.
1. What is Relation's unconventional approach to drug discovery?
- Lindsay Edwards, a former musician turned drug hunter, embodies Relation's bold mission to transform drug discovery through multidisciplinary expertise bridging biology, data science, and creative intuition.
- Relation harnesses human genetic insights and single-cell data to pinpoint superior drug targets, addressing a critical bottleneck in pharmaceutical R&D.
2. How does Relation integrate lab experiments and machine learning?
- Relation's "Lab-in-the-Loop" approach tightly integrates wet lab experiments with machine learning, iteratively refining models that map genetic variation to disease mechanisms.
- Interdisciplinary collaboration between biologists, data scientists, and computational experts catalyzes this symbiosis of lab and AI.
3. How does Relation's initial focus in developing precision medicines for osteoporosis open opportunities in other therapuetic areas?
- By studying osteoblasts and genetic determinants of bone density, Relation aims to develop anabolic therapies driving new bone growth, addressing the large unmet need in osteoporosis with precision medicine.
- Their traction in osteoporosis shows how Relation can apply biological signals to improve machine learning modeling, uncover novel therapeutic targets.
To learn more about Relation and their transformative approach to drug discovery, visit their website: https://www.relationrx.com/
Get in touch with AWS here to learn how we can help your organization accelerate healthcare innovation.
Please take a moment and let us know what you think of the podcast, access our feedback survey here.
117 פרקים
Manage episode 441652004 series 3291628
In this episode of the AWS Health Innovation Podcast, host Yin He, Principal Business Development for Healthcare & Life Sciences Startups at AWS, sits down with Lindsay Edwards, CTO and President of Platform at Relation, a biotech company leveraging single-cell multi-omics, machine learning, and clinical insights to develop transformational medicines.
1. What is Relation's unconventional approach to drug discovery?
- Lindsay Edwards, a former musician turned drug hunter, embodies Relation's bold mission to transform drug discovery through multidisciplinary expertise bridging biology, data science, and creative intuition.
- Relation harnesses human genetic insights and single-cell data to pinpoint superior drug targets, addressing a critical bottleneck in pharmaceutical R&D.
2. How does Relation integrate lab experiments and machine learning?
- Relation's "Lab-in-the-Loop" approach tightly integrates wet lab experiments with machine learning, iteratively refining models that map genetic variation to disease mechanisms.
- Interdisciplinary collaboration between biologists, data scientists, and computational experts catalyzes this symbiosis of lab and AI.
3. How does Relation's initial focus in developing precision medicines for osteoporosis open opportunities in other therapuetic areas?
- By studying osteoblasts and genetic determinants of bone density, Relation aims to develop anabolic therapies driving new bone growth, addressing the large unmet need in osteoporosis with precision medicine.
- Their traction in osteoporosis shows how Relation can apply biological signals to improve machine learning modeling, uncover novel therapeutic targets.
To learn more about Relation and their transformative approach to drug discovery, visit their website: https://www.relationrx.com/
Get in touch with AWS here to learn how we can help your organization accelerate healthcare innovation.
Please take a moment and let us know what you think of the podcast, access our feedback survey here.
117 פרקים
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