GraphMatch: Knowledge Graphs for Allogeneic Stem Cell Matching
Kunau, T. M.
Show abstract
Allogeneic bone marrow and umbilical cord stem cell transplants often provide the best hope for curing many patients with leukemia, lymphoma, and over 70 other diseases. Matching patients to unrelated donors requires flexible and timely searches as matching criteria change. Matching systems should scale to accommodate the diversity in patient and donor typing resolution and the growing number of donors. We developed GraphMatch (GM), a scalable graph database solution for storing and searching variable-resolution HLA genotype markers. As a test set, we expanded the World Marrow Donor Association (WMDA) validation set based on the IPD-IMGT/HLA Database version 2.16 to create a synthetic production data set of 1 million patients and 10 million donors. Single-patient identity search times range from 218.5 milliseconds per patient for 2 million donors to 1201.4 milliseconds per patient for 10 million donors. Search performance timing remained linear with the number of edges, even at a production scale. In general, GM demonstrates the usefulness of graph databases as a flexible platform for scalable matching solutions.
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"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.