EP102: Dr. Marco Schmidt, founder and Chief Scientific Officer of BioTx.ai, on how to use artificial intelligence and machine learning in genomics research
Jul 06, 2023•38 hr 36 min•Transcript available on Metacast Episode description
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
