Unlocking the Potential of Protein Prediction in Drug Discovery

Abstract: 

This session will introduce attendees to the benefits and challenges of using machine learning for biotech and pharmaceutical applications. We will discuss various machine learning techniques and how they can be used to drive innovation in drug discovery, disease diagnosis, genomics, and personalized medicine. We will also explore the need for large datasets and the complexity of evaluating model performance. Attendees of this session will gain an understanding of the potential of machine learning to revolutionize the way biotechnology and pharmaceutical companies develop new therapeutics.

Bio: 

Tomasz Adamusiak MD Ph.D. is a Chief Scientist in the Clinical Insights & Innovation Cell at MITRE. He leads a multi-disciplinary group driving high-impact contributions to private and public sectors in Clinical and Genomic Data Science. Before MITRE, Tomasz was the Head of Data Science in the Pfizer Innovation Research (PfIRe) Lab. His team was responsible for developing novel digital endpoints, designing decentralized approaches for clinical trials, and applying AI/machine learning methods to generate novel insights from clinical data. Tomasz served in leadership and advisory roles in the American Medical Informatics Association, the SNOMED International, and the Epic Research Data Network.

Open Data Science

 

 

 

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