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Found 6 papers, 1 papers with code
Towards future directions in data-integrative supervised prediction of human aging-related genes
Here, we evaluate whether analyzing a weighted dynamic aging-specific subnetwork inferred from newer GE and PPIN data improves prediction accuracy upon analyzing the best current subnetwork inferred from outdated data.
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).
Improved supervised prediction of aging-related genes via weighted dynamic network analysis
Instead, we recently inferred a dynamic aging-specific subnetwork using a methodologically more advanced notion of network propagation (NP), which improved upon Induced dynamic aging-specific subnetwork in a different task, that of unsupervised analyses of the aging process.
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.
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.
