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The Best Way to Leverage Public Omics Datasets

In the era of big data and predictive modeling in life sciences, the integration and analysis of multi-omics datasets have emerged as powerful tools for deciphering the intricate molecular mechanisms underlying biological systems. 

This type of data is crucial in life sciences, particularly for applications using machine learning. Analyzing diverse data types together reveals hidden patterns, providing comprehensive insights. This data can be very complex though, so exploring analysis opportunities and enhancing data accessibility standards is crucial for users.

In this webinar you will explore: 

  • The process of bringing multi-omic datasets from data generation to discovery
  • How you can improve the use of these datasets in organization and exploration
  • Challenges in accessing public datasets in academic and commercial settings 
  • Best practices for organizing datasets, standardized formatting, and access methods for future use
  • Methods of exploration for discoveries that could empower scientists of various domains

Speakers

Emily So

Bioinformatics Scientist

Emily So

Bioinformatics Scientist

Emily So

Emily So, a Bioinformatics Scientist at Code Ocean. Emily is part of the Customer Success Team, aiding in advanced customer technical use cases. She just recently defended her Masters degree in Computational Biology at the University of Toronto, specializing in Machine-Learning for single-cell genomics and protein-protein interactions.

Benjamin Haibe-Kains

Senior Scientist at Princess Margaret Cancer Centre

Benjamin Haibe-Kains

Senior Scientist at Princess Margaret Cancer Centre

Benjamin Haibe-Kains

Dr. Benjamin Haibe-Kains is a Senior Scientist at the Princess Margaret Cancer Centre and Professor in the Medical Biophysics Department of the University of Toronto. He earned his PhD in Bioinformatics at the University of Belgium. Supported by a Fulbright Award, he did his postdoctoral fellowship at the Dana-Farber Cancer Institute and Harvard School of Public Health (USA). Dr. Haibe-Kains’ research focuses on the integration of high-throughput data from various sources to simultaneously analyze multiple facets of carcinogenesis.