AI-augmented mechanistic target identification at BenevolentAI


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We are witnessing an exponential increase in the availability of biomedical data and published research findings. Despite this vast and expanding collective dataset, human diseases remain a significant unsolved problem due to limitations in how we translate basic research into effective treatments.

BenevolentAI has built a Knowledge Graph that integrates large quantities of diverse and independent biomedical data sources - including over 35 million scientific papers, terabytes of genetics and genomics data and the contents of dozens of structured databases - to capture a detailed mechanistic representation of the dysregulated processes that underlie human disease. The Knowledge Graph can be explored or modelled using machine learning tools to uncover novel disease targets. In this talk, Gabriel Rosser will detail the AI-driven process at BenevolentAI to identify therapeutic targets and uncover more effective disease-modifying treatments.

Gabriel Rosser

Lead Bioinformatics Data Scientist

BenevolentAI

Dr Gabriel Rosser is a lead bioinformatics data scientist at BenevolentAI. He holds a DPhil in mathematical biology from the University of Oxford and spent several years engaged in postdoctoral research applying integrative omics analysis to the study of the brain tumour glioblastoma. His interests include statistical analysis and modelling of genetics and genomics data.

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AI-augmented mechanistic target identification at BenevolentAI
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Open to view video. We are witnessing an exponential increase in the availability of biomedical data and published research findings. Despite this vast and expanding collective dataset, human diseases remain a significant unsolved problem due to limitations in how we translate basic research into effective treatments. BenevolentAI has built a Knowledge Graph that integrates large quantities of diverse and independent biomedical data sources - including over 35 million scientific papers, terabytes of genetics and genomics data and the contents of dozens of structured databases - to capture a detailed mechanistic representation of the dysregulated processes that underlie human disease. The Knowledge Graph can be explored or modelled using machine learning tools to uncover novel disease targets. In this talk, Gabriel Rosser will detail the AI-driven process at BenevolentAI to identify therapeutic targets and uncover more effective disease-modifying treatments.
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