1 citations · 3 across the 3 of their papers we have counts for
5 papers
An Open-Source Knowledge Graph Ecosystem for the Life Sciences
Tiffany J. Callahan, Ignacio J. Tripodi, Adrianne L. Stefanski +29
Translational research requires data at multiple scales of biological organization. Advancements in sequencing and multi-omics technologies have increased the availability of these…
Ontologizing Health Systems Data at Scale: Making Translational Discovery a Reality
Tiffany J. Callahan, Adrianne L. Stefanski, Jordan M. Wyrwa +29
Background: Common data models solve many challenges of standardizing electronic health record (EHR) data, but are unable to semantically integrate all the resources needed for dee…
A method for comparing multiple imputation techniques: a case study on the U.S. National COVID Cohort Collaborative
Elena Casiraghi, Rachel Wong, Margaret Hall +34
Healthcare datasets obtained from Electronic Health Records have proven to be extremely useful to assess associations between patients' predictors and outcomes of interest. However…
GRAPE for Fast and Scalable Graph Processing and random walk-based Embedding
Luca Cappelletti, Tommaso Fontana, Elena Casiraghi +8
Graph Representation Learning (GRL) methods opened new avenues for addressing complex, real-world problems represented by graphs. However, many graphs used in these applications co…
Het-node2vec: second order random walk sampling for heterogeneous multigraphs embedding
Mauricio Soto-Gomez, Peter Robinson, Carlos Cano +6
Many real-world problems are naturally modeled as heterogeneous graphs, where nodes and edges represent multiple types of entities and relations. Existing learning models for heter…