activity
20132026
most citedModeling polypharmacy side effects with graph convolutional networks

1.5k citations · 6.9k across the 102 of their papers we have counts for

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Showing q-bio.MNShow all

5 papers · 1 filter

q-bio.MN2023★ 108 cited

Current and future directions in network biology

Marinka Zitnik, Michelle M. Li, Aydin Wells +34

Network biology is an interdisciplinary field bridging computational and biological sciences that has proved pivotal in advancing the understanding of cellular functions and diseas…

q-bio.MN2020★ 6 cited

SkipGNN: Predicting Molecular Interactions with Skip-Graph Networks

Kexin Huang, Cao Xiao, Lucas Glass +2

Molecular interaction networks are powerful resources for the discovery. They are increasingly used with machine learning methods to predict biologically meaningful interactions. W…

q-bio.MN2020★ 116 cited

Network Medicine Framework for Identifying Drug Repurposing Opportunities for COVID-19

Deisy Morselli Gysi, Ítalo Do Valle, Marinka Zitnik +8

The current pandemic has highlighted the need for methodologies that can quickly and reliably prioritize clinically approved compounds for their potential effectiveness for SARS-Co…

q-bio.MN2018★ 164 cited

Network Enhancement: a general method to denoise weighted biological networks

Bo Wang, Armin Pourshafeie, Marinka Zitnik +4

Networks are ubiquitous in biology where they encode connectivity patterns at all scales of organization, from molecular to the biome. However, biological networks are noisy due to…

q-bio.MN2017

Large-scale analysis of disease pathways in the human interactome

Monica Agrawal, Marinka Zitnik, Jure Leskovec

Discovering disease pathways, which can be defined as sets of proteins associated with a given disease, is an important problem that has the potential to provide clinically actiona…