Search Results for author: Arjun Krishnan

Found 6 papers, 4 papers with code

Accurately Modeling Biased Random Walks on Weighted Graphs Using $\textit{Node2vec+}$

1 code implementation15 Sep 2021 Renming Liu, Matthew Hirn, Arjun Krishnan

$\textit{Node2vec}$ is a widely used method for node embedding that works by exploring the local neighborhoods via biased random walks on the graph.

Geodesic length and shifted weights in first-passage percolation

no code implementations29 Jan 2021 Arjun Krishnan, Firas Rassoul-Agha, Timo Seppäläinen

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

Reconciling Multiple Connectivity Scores for Drug Repurposing

2 code implementations19 Sep 2020 Kewalin Samart, Phoebe Tuyishime, Arjun Krishnan, Janani Ravi

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.

PecanPy: A parallelized, efficient, and accelerated node2vec in Python

1 code implementation23 Jul 2020 Renming Liu, Arjun Krishnan

Learning low-dimensional representations (embeddings) of nodes in large graphs is key to applying machine learning on massive biological networks.

Supervised learning is an accurate method for network-based gene classification

1 code implementation1 Jun 2020 Renming Liu, Christopher A Mancuso, Anna Yannakopoulos, Kayla A Johnson, Arjun Krishnan

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.

General Classification

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