6 citations · 9 across the 4 of their papers we have counts for
16 papers
Modeling multi-scale data via a network of networks
Shawn Gu, Meng Jiang, Pietro Hiram Guzzi +1
Prediction of node and graph labels are prominent network science tasks. Data analyzed in these tasks are sometimes related: entities represented by nodes in a higher-level (higher…
Dynamic network analysis improves protein 3D structural classification
Khalique Newaz, Jacob Piland, Patricia L. Clark +3
Protein structural classification (PSC) is a supervised problem of assigning proteins into pre-defined structural (e.g., CATH or SCOPe) classes based on the proteins' sequence or 3…
Improved supervised prediction of aging-related genes via weighted dynamic network analysis
Qi Li, Khalique Newaz, Tijana Milenković
This study focuses on the task of supervised prediction of aging-related genes from -omics data. Unlike gene expression methods for this task that capture aging-specific informatio…
Data-driven biological network alignment that uses topological, sequence, and functional information
Shawn Gu, Tijana Milenkovic
Many proteins remain functionally unannotated. Sequence alignment (SA) uncovers missing annotations by transferring functional knowledge between species' sequence-conserved regions…
Weighted graphlets and deep neural networks for protein structure classification
Hongyu Guo, Khalique Newaz, Scott Emrich +2
As proteins with similar structures often have similar functions, analysis of protein structures can help predict protein functions and is thus important. We consider the problem o…
The power of dynamic social networks to predict individuals' mental health
Shikang Liu, David Hachen, Omar Lizardo +3
Precision medicine has received attention both in and outside the clinic. We focus on the latter, by exploiting the relationship between individuals' social interactions and their…