Publications & Media


Himmelstein DS & Baranzini SE (2015) Heterogeneous Network Edge Prediction: A Data Integration Approach to Prioritize Disease-Associated Genes. PLOS Computational Biology doi:10.1371/journal.pcbi.1004259

Poster from ASHG 2014 also at F1000Posters

Article in Stanford's Biomedical Computation Review

Heterogeneous Network Visualization takes Silver

Our visualization took second place in the Most Aesthetically Pleasing category of the Cytoscape 3.2 Lauch Challenge. The results were announced on December 24, 2014.

Title: Heterogeneous Network Visualization with Cytoscape: An Integrative Approach to the Genetics of Complex Human Disease

Description: To provide context for understanding disease-gene associations, we integrated public resources into a heterogeneous network. The network contains 40,343 nodes (across 18 types) and 1,608,168 edges (across 19 types). Using the network's topology, we learned influential mechanisms underlying pathogenesis and predicted the probability that each gene was associated with each disease.

ISMB Late Breaking Research Video Proposal

A short video describing the use of HNEP for predicting disease-associated genes.