Search Results for author:
Found 6 papers, 4 papers with code
RNA-KG: An ontology-based knowledge graph for representing interactions involving RNA molecules
The resource can be easily updated with new experimental data, and specific views of the overall KG can be extracted according to the bio-medical problem to be studied.
An Open-Source Knowledge Graph Ecosystem for the Life Sciences
Translational research requires data at multiple scales of biological organization.
Ontologizing Health Systems Data at Scale: Making Translational Discovery a Reality
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 deep phenotyping.
A method for comparing multiple imputation techniques: a case study on the U.S. National COVID Cohort Collaborative
In this paper, we propose a novel framework to numerically evaluate strategies for handling missing data in the context of statistical analysis, with a particular focus on multiple imputation techniques.
GRAPE for Fast and Scalable Graph Processing and random walk-based Embedding
Graph Representation Learning (GRL) methods opened new avenues for addressing complex, real-world problems represented by graphs.
Het-node2vec: second order random walk sampling for heterogeneous multigraphs embedding
The development of Graph Representation Learning methods for heterogeneous graphs is fundamental in several real-world applications, since in several contexts graphs are characterized by different types of nodes and edges.
