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