EP102: Dr. Marco Schmidt, founder and Chief Scientific Officer of BioTx.ai, on how to use artificial intelligence and machine learning in genomics research

0:00 Intro 0:45 The founding of BioTx.ai 4:35 How do algorithms for ‘causal inference’ work? 6:30 Modeling gene interactions for genetic disorders 8:35 How to predict gene interactions 10:30 What happens after identifying a potential gene variant or interaction? 14:35 How can you use machine learning to determine causal relationships between gene variants and disease? 17:30 Deconvoluting genes and traits, and their impacts on effect size 19:20 Key ingredients in determining causal relationships: data and computational power 21:10 Limitations of using machine learning to find genetic determinants of rare diseases 24:30 Predicting clinical outcomes with Biotx.ai 28:05 Machine learning enhances efficiency in the pre-clinical phase 29:40 Population genomics in Germany 32:50 Marco’s career decisions – starting a company vs. continuing in academia 35:50 The pros and cons of industry 38:10 The gaps in industry and academia 41:20 Closing remarks