Detecting subnetwork-level dynamic correlations

By Yan Yan, Shan Qiu, Zhuxuan Jin, Sihong Gong, Yun Bai, Jianwei Lu, Tianwei Yu. 2017

12 citations recorded by the source corpus.

Subfields: Molecular Biology, Statistics and Probability, Neuroscience

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Molecular Biology

1. Marinka Zitnik
2. Jure Leskovec
3. Jian Tang
4. Weihua Hu
5. Bowen Liu
6. Percy Liang
7. Joseph Gomes
8. Vijay S. Pande

In Statistics and Probability

1. Percy Liang
2. Jian Tang
3. Jure Leskovec
4. Vijay S. Pande
5. Marinka Zitnik
6. Weihua Hu

Citations in the corpus

• Strategies for Pre-training Graph Neural Networks (2019, background): “biology, identifying the functionality of proteins is important to find proteins that associate with a certain disease, where proteins are represented by local protein-protein interaction (PPI) graphs [57, 55]. Supervised learning of graphs, especially with Graph Neural Networks (GNNs) [26, 15, 59, 5”
• InfoGraph: Unsupervised and Semi-supervised Graph-Level Representation Learning via Mutual Information Maximization (2019, background): “One of the biggest challenges in prediction tasks in biology [62, 64] or molecular machine learning [10, 15, 19] is the extreme scarcity of labeled data.”
• Pre-training Graph Neural Networks (2019, background): “In biology, identifying the functionality of proteins is important to find proteins that associate with a certain disease, where proteins are represented by local protein-protein interaction (PPI) graphs [57, 55].”