EP102: Dr. Marco Schmidt, founder and Chief Scientific Officer of BioTx.ai, on how to use artificial intelligence and machine learning in genomics research
Update: 2023-07-06
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
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
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