Applications of Biomedical Knowledge Graph for AI and Machine Learning
About this talk
Biomedical knowledge graphs (BMKGs) link biomedical entities (such as diseases, proteins, and drugs) through certain defined relationships. They are important tools to computationally analyze the comprehensive body of biomedical knowledge.
In this webinar, Anton Yuryev, Biology Director at Elsevier will talk about the approaches and use of knowledge graph together with artificial Intelligence algorithms for various biomedical applications such as drug repurposing, personalized drug therapy, and personalized immunotherapy.
The talk will cover
- comparing different graph embedding techniques
- introducing a new graph embedding technique that uses patient OMICs data to calculate node activity using sub-network enrichment analysis
- how to use node activity to find likely disease mechanism in a patient and use this mechanism to predict personalized treatment or neoantigen vaccine design, or new target discovery
Dr. Anton Yuryev has PhD in Genetics from Johns Hopkins University where he discovered proteins physically linking gene transcription with mRNA processing in eukaryotic cells. He worked over 30 years in bioinformatics as Senior Scientist at InforMax, as Senior Bioinformatics Analyst at Orchid Cellmark, and as Senior Director of Application Science at Ariadne Genomics. Dr. Yuryev published over 50 scientific articles, edited four scientific books, authored algorithms for primer design and pathway analysis. He currently serves as Professional Services Director at Elsevier and responsible for development of targeted bioinformatics solutions using Elsevier proprietary software, knowledgebases and artificial intelligence in the areas of drug development, personalized precision medicine, agro- and synthetic biology.