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"Unlocking the Glycoproteome: Utilizing a Robust AI/ML Based Platform for Biomarker Discovery"

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Liquid biopsies have shown tremendous progress for the early detection of cancer and for treatment monitoring, but still have limitations in sensitivity and specificity. The inclusion of protein glycosylation can help address these challenges.

Glycoproteomic analysis is now emerging as a promising tool. These biomarkers have the potential to play a crucial role in early intervention and personalized therapy decisions. In the past, the complexity of data generated using mass spectrometry - which is the most reliable method for identifying glycoproteins - made it difficult to analyze the glycoproteome at scale.

Join us for this insightful discussion about the latest solutions for combining robust assays with artificial intelligence and machine learning to accelerate data processing, overcome current barriers, and provide crucial information that is missing from the existing -omics landscape. We’ll explore real-world examples with a look at:

• Prediction of response to immunotherapy
• Impact of glycosylation on antibody binding
• How using a multi-omic knowledge graph can lead to new insights into disease biology

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