AI and Statistics in Proteomics and Systems Biology

Professor Olga Vitek has a deep understanding of statistics, machine learning, and computational biology. She puts her know-how to work to develop computational tools enabling high-quality proteomic analysis and systems biology approaches. She hopes to apply these tools to the quantitative analysis of large-scale mass spectrometry-based investigations and thereby advance our understanding of organismal function. In this episode, Parag and Professor Vitek discuss:

1. Why statistics is important for experimental design
2. How statistics and AI can help researchers understand biology
3. Gaps keeping us from using AI and statistics to their maximum potential in biology

Resources

Statistical methods for studies of biomolecular systems website

1. Olga’s personal lab website

Beyond protein lists: AI-assisted interpretation of proteomic investigations in the context of evolving scientific knowledge

1. Gyori and Vitek, 2024 discuss how AI can be used to interpret proteomics data and its biological meaning.

A Bayesian Active Learning Experimental Design for Inferring Signaling Networks

1. Ness et al., 2018 show how statistical methods can guide the selection of experiments that optimally enhance understanding