no code implementations • 15 Sep 2023 • Marinka Zitnik, Michelle M. Li, Aydin Wells, Kimberly Glass, Deisy Morselli Gysi, Arjun Krishnan, T. M. Murali, Predrag Radivojac, Sushmita Roy, Anaïs Baudot, Serdar Bozdag, Danny Z. Chen, Lenore Cowen, Kapil Devkota, Anthony Gitter, Sara Gosline, Pengfei Gu, Pietro H. Guzzi, Heng Huang, Meng Jiang, Ziynet Nesibe Kesimoglu, Mehmet Koyuturk, Jian Ma, Alexander R. Pico, Nataša Pržulj, Teresa M. Przytycka, Benjamin J. Raphael, Anna Ritz, Roded Sharan, Yang shen, Mona Singh, Donna K. Slonim, Hanghang Tong, Xinan Holly Yang, Byung-Jun Yoon, Haiyuan Yu, Tijana Milenković
As such, it is expected to help shape short- and long-term vision for future computational and algorithmic research in network biology.
1 code implementation • 15 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.
no code implementations • 12 Feb 2021 • Ian Alevy, Arjun Krishnan
We consider i. i. d.
Probability 60K35, 60K37
no code implementations • 29 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
2 code implementations • 19 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.
1 code implementation • 23 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.
1 code implementation • 1 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.