Structural Genomics Consortium (SGC) Appoints Benjamin Haibe-Kains as Head of Data Science
[Toronto, January 10, 2024] - Professor Benjamin Haibe-Kains has joined the Structural Genomics Consortium as the Head of Data Science to further support SGC’s ambition to generate high-quality and reproducible data. In his new role, Dr. Haibe-Kains will establish a robust mechanism to ensure transparency, interoperability, and accessibility of SGC’s data for machine learning and pattern recognition.
The Structural Genomics Consortium is a global public-private partnership that seeks to accelerate drug discovery by fostering collaboration among a large network of scientists in academia and industry and making all research outputs openly available to the scientific community. The current SGC research sites are located at Goethe University in Frankfurt, Karolinska Institute, McGill University, University College, London, the University of North Carolina, Chapel Hill, and the University of Toronto. The SGC-Toronto laboratory, under the direction of Professor Cheryl Arrowsmith, operates within the Temerty Faculty of Medicine at the University of Toronto and is located in the MaRS Discovery District adjacent to Princess Margaret Cancer Centre, University Health Network, where Professors Haibe-Kains and Arrowsmith hold Senior Scientist appointments.
Dr. Haibe-Kains obtained his PhD in Bioinformatics from the Université Libre de Bruxelles (Belgium). Supported by a Fulbright Award, he did his postdoctoral fellowship at the Dana-Farber Cancer Institute and Harvard School of Public Health (USA). He is currently a Senior Scientist at the Princess Margaret Cancer Centre, University Health Network, and a Professor in the Department of Medical Biophysics at the Temerty Faculty of Medicine at the University of Toronto. Additionally, he is the Scientific Director at Cancer Digital Intelligence and Infrastructure Lead at the Temerty Centre for Artificial Intelligence Research and Education in Medicine (T-CAIREM), contributing to the advancement of artificial intelligence in medicine.
His lab focuses on integrating high-throughput data from diverse sources to simultaneously analyze multiple facets of carcinogenesis. His team is developing new prognostic and predictive models by analyzing radiological and (pharmaco)genomic datasets, aiming to improve drug development and disease management through novel therapeutic strategies.
Dr. Haibe-Kains is committed to making high-quality data globally accessible. As he leads efforts to enhance transparency and reproducibility in computational research, he also maintains multiple public genomic datasets and open-source software packages for the scientific community.
At the SGC, Dr. Haibe-Kains will spearhead the development of a robust data architecture, ensuring that high-quality data is stored efficiently and in a machine-learning-friendly format. More specifically, he will lead the development of AIRCHECK - the Artificial Intelligence-Ready CHEmiCal Knowledge Base. This innovative database will convert over 50 million data points from SGC's experimental Cores and partners into a format suitable for machine learning. AIRCHECK is poised to become a significant open data resource, containing over 300 terabytes of high-quality, experimentally validated protein-small molecule binding data. Adhering to the FAIR principles (Findable, Accessible, Interoperable, Reusable), AIRCHECK, a cloud-based database, is specifically designed for data scientists.
Having Dr. Haibe-Kains onboard to lead this project aligns with SGC's vision to provide open access to valuable experimental data, thereby accelerating AI-guided early drug discovery.
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