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Found 7 papers, 4 papers with code
PecanPy: A parallelized, efficient, and accelerated node2vec in Python
Learning low-dimensional representations (embeddings) of nodes in large graphs is key to applying machine learning on massive biological networks.
Accurately Modeling Biased Random Walks on Weighted Graphs Using $\textit{Node2vec+}$
$\textit{Node2vec}$ is a widely used method for node embedding that works by exploring the local neighborhoods via biased random walks on the graph.
Supervised learning is an accurate method for network-based gene classification
Results: In this study, we present a comprehensive benchmarking of supervised learning for network-based gene classification, evaluating this approach and a classic label propagation technique on hundreds of diverse prediction tasks and multiple networks using stringent evaluation schemes.
Reconciling Multiple Connectivity Scores for Drug Repurposing
The basis of several recent methods for drug repurposing is the key principle that an efficacious drug will reverse the disease molecular 'signature' with minimal side-effects.
Geodesic length and shifted weights in first-passage percolation
This puts into a convex duality framework old observations about the convergence of the normalized Euclidean length of geodesics due to Hammersley and Welsh, Smythe and Wierman, and Kesten, and leads to new results about geodesic length and the regularity of the shape function as a function of the weight shift.
Probability 60K35, 60K37
Current and future directions in network biology
As such, it is expected to help shape short- and long-term vision for future computational and algorithmic research in network biology.
