20 citations · 97 across the 23 of their papers we have counts for
19 papers · 1 filter
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…
Network analysis of synonymous codon usage
Khalique Newaz, Gabriel Wright, Jacob Piland +4
Most amino acids are encoded by multiple synonymous codons. For an amino acid, some of its synonymous codons are used much more rarely than others. Analyses of positions of such ra…
Data-driven network alignment
Shawn Gu, Tijana Milenkovic
Biological network alignment (NA) aims to find a node mapping between species' molecular networks that uncovers similar network regions, thus allowing for transfer of functional kn…
Network-based protein structural classification
Khalique Newaz, Mahboobeh Ghalehnovi, Arash Rahnama +2
Experimental determination of protein function is resource-consuming. As an alternative, computational prediction of protein function has received attention. In this context, prote…
Pairwise versus multiple network alignment
Vipin Vijayan, Shawn Gu, Eric Krebs +2
Biological network alignment (NA) aims to identify similar regions between molecular networks of different species. NA can be local or global. Just as the recent trend in the NA fi…
From homogeneous to heterogeneous network alignment via colored graphlets
Shawn Gu, John Johnson, Fazle E. Faisal +1
Network alignment (NA) compares networks with the goal of finding a node mapping that uncovers highly similar (conserved) network regions. Existing NA methods are homogeneous, i.e.…
Network approach integrates 3D structural and sequence data to improve protein structural comparison
Fazle E. Faisal, Julie L. Chaney, Khalique Newaz +4
Initial protein structural comparisons were sequence-based. Since amino acids that are distant in the sequence can be close in the 3-dimensional (3D) structure, 3D contact approach…
Multiple network alignment via multiMAGNA++
Vipin Vijayan, Tijana Milenkovic
Network alignment (NA) aims to find a node mapping between molecular networks of different species that identifies topologically or functionally similar network regions. Analogous…
IGLOO: Integrating global and local biological network alignment
Lei Meng, Joseph Crawford, Aaron Striegel +1
Analogous to genomic sequence alignment, biological network alignment (NA) aims to find regions of similarities between molecular networks (rather than sequences) of different spec…
Local versus Global Biological Network Alignment
Lei Meng, Aaron Striegel, Tijana Milenkovic
Network alignment (NA) aims to find regions of similarities between molecular networks of different species. There exist two NA categories: local (LNA) or global (GNA). LNA finds s…
GREAT: GRaphlet Edge-based network AlignmenT
Joseph Crawford, Tijana Milenković
Network alignment aims to find regions of topological or functional similarities between networks. In computational biology, it can be used to transfer biological knowledge from a…
Simultaneous Optimization of Both Node and Edge Conservation in Network Alignment via WAVE
Yihan Sun, Joseph Crawford, Jie Tang +1
Network alignment can be used to transfer functional knowledge between conserved regions of different networks. Typically, existing methods use a node cost function (NCF) to comput…
Fair Evaluation of Global Network Aligners
Joseph Crawford, Yihan Sun, Tijana Milenković
Biological network alignment identifies topologically and functionally conserved regions between networks of different species. It encompasses two algorithmic steps: node cost func…
MAGNA: Maximizing Accuracy in Global Network Alignment
Vikram Saraph, Tijana Milenković
Biological network alignment aims to identify similar regions between networks of different species. Existing methods compute node "similarities" to rapidly identify from possible…
Identifying edge clusters in networks via edge graphlet degree vectors (edge-GDVs) and edge-GDV-similarities
Ryan W. Solava, Ryan P. Michaels, Tijana Milenkovic
Inference of new biological knowledge, e.g., prediction of protein function, from protein-protein interaction (PPI) networks has received attention in the post-genomic era. A popul…
An integrative approach to modeling biological networks
Vesna Memisevic, Tijana Milenkovic, Natasa Przulj
Since proteins carry out biological processes by interacting with other proteins, analyzing the structure of protein-protein interaction (PPI) networks could explain complex biolog…