What exactly is a ‘graph’?
A graph is a modeling technique for complex environments. It thrives best in modeling relationships between entities. Example: Two different objects, a patient and a disease, both are represented as a node in space. If the patient has that disease a connection between the two is made.
Why a graph?
For any kind of machine learning application the most important part is data. Being in the field of rare diseases this data unfortunately does not exist in suitable quantities. The idea is to leverage everything we have learned about human biology, disease, drugs, etc, to make up for this lack of data. Thus enabling the use of machine learning algorithms. All this background knowledge is best modeled via relationships between entities, hence the usage of the graph.
What kind of information is in our graph?
The graph used in this project is an evolution of the ‘clinicalknowledgegraph’ (https://ckg.readthedocs.io/en/latest/). It was originally designed for use in proteomics and was modified to fit the task at hand. A detailed wiki on the graph, its structure, and the different node classes can be provided to you. Please reach out to Henrik.Otterstedt@med.uni-muenchen.de.
What are the benefits for my clinic?
By incorporating the AMIGO approach into your clinic, we want to enable you to achieve the following goals:
- Assist in the diagnosis of rare diseases. For immediate use in the clinic, the aim is to assist in the diagnosis and treatment of rare disease patients. The idea is to find similarities with existing patients and use the background to identify causal genes. This will be done not only in Munich, but also in partner hospitals throughout Europe.
- Working with pharmaceutical companies to identify patients for clinical trials: Another goal is to identify patients who might be suitable for a clinical trial by a pharmaceutical company.