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Found 10 papers, 1 papers with code
Modeling multi-scale data via a network of networks
Prediction of node and graph labels are prominent network science tasks.
Dynamic network analysis improves protein 3D structural classification
Here, we propose for the first time a way to model 3D structures of proteins as dynamic PSNs, with the hypothesis that this will improve upon the current state-of-the-art PSC approaches that are based on static PSNs (and thus upon the existing state-of-the-art sequence and other 3D structural approaches).
Data-driven biological network alignment that uses topological, sequence, and functional information
Existing NA assumes that it is topological similarity (isomorphic-like matching) between network regions that corresponds to the regions' functional relatedness.
Weighted graphlets and deep neural networks for protein structure classification
We develop a weighted network that depicts the protein structures, and more importantly, we propose the first graphlet-based measure that applies to weighted networks.
The power of dynamic social networks to predict individuals' mental health
Precision medicine has received attention both in and outside the clinic.
Heterogeneous network approach to predict individuals' mental health
To identify individuals who are vulnerable to depression and anxiety, predictive models have been built that typically utilize data from one source.
Data-driven network alignment
A likely reason is that they assume it is topologically similar nodes that are functionally related.
Inference of the Dynamic Aging-related Biological Subnetwork via Network Propagation
We use prominent existing NP methods in a new task of inferring a dynamic rather than static condition-specific (aging-related) subnetwork.
Network-based protein structural classification
Experimental determination of protein function is resource-consuming.
Pairwise versus multiple network alignment
Just as the recent trend in the NA field, we also focus on global NA, which can be pairwise (PNA) and multiple (MNA).
