[ismb2022 tutorial] the picture shows the precision medicine of learning. Marinka zitnik, Harvard University, keynote speaker, with 87 ppt

The contents of the catalog ：

• The first 1 part : summary : Introduce graph representation learning for biomedicine
• The first 2 part : Method : Neural messaging , Figure neural network , Equivariant neural network
• The first 3 part : application : pre

         Reasoning about disease , Including methods of injecting transcriptome data into protein interaction networks to identify candidate biomarkers of disease progression , Modeling the impact of non coding regions on disease , And non coding RNA Interactions are incorporated into protein interaction networks to capture genome-wide associations .

         Reasoning about treatment , Including molecular graph modeling methods of small molecular compounds , medicine - Drugs and drugs - Quantification of target interaction , And identify drugs - Association of diseases and biomarkers of complex diseases .

         Reasoning about precision medicine , Pay special attention to personalized medical knowledge network and patient records .

• The first 4 part : expectation : Future development direction and Q & a link

         Practice : demonstration 、 Implementation details 、 Tools and techniques . We will cover the following materials :

         Map machine learning tutorial of precision medicine

         Graph machine learning tutorial for therapeutic Science

Download link ：

https://zitniklab.hms.harvard.edu/biomedgraphml/