Search Results for author:
Found 9 papers, 4 papers with code
Quantifying the Impact of Biobanks and Cohort Studies
We show that, on average, 41. 1\% of articles miss to reference any of the biobank's reference papers and 59. 6\% include a biobank member as a co-author.
Hidden Citations Obscure True Impact in Science
References, the mechanism scientists rely on to signal previous knowledge, lately have turned into widely used and misused measures of scientific impact.
Neuroscience needs Network Science
We explore the challenges and opportunities in integrating multiple data streams for understanding the neural transitions from development to healthy function to disease, and discuss the potential for collaboration between network science and neuroscience communities.
Network medicine framework reveals generic herb-symptom effectiveness of Traditional Chinese Medicine
We validate our framework with real-world hospital patient data by showing that (1) shorter network distance between symptoms of inpatients correlates with higher relative risk (co-occurrence), and (2) herb-symptom network proximity is indicative of patients' symptom recovery rate after herbal treatment.
AI-Bind: Improving Binding Predictions for Novel Protein Targets and Ligands
Identifying novel drug-target interactions (DTI) is a critical and rate limiting step in drug discovery.
3D Topology Transformation with Generative Adversarial Networks
Finally, we compare the results between our approach and a baseline algorithm that directly convert the 3D shapes, without using our GAN.
Finding Patient Zero: Learning Contagion Source with Graph Neural Networks
% We observe that GNNs can identify P0 close to the theoretical bound on accuracy, without explicit input of dynamics or its parameters.
Network Medicine Framework for Identifying Drug Repurposing Opportunities for COVID-19
The current pandemic has highlighted the need for methodologies that can quickly and reliably prioritize clinically approved compounds for their potential effectiveness for SARS-CoV-2 infections.
Understanding the Representation Power of Graph Neural Networks in Learning Graph Topology
We find that GCNs are rather restrictive in learning graph moments.
