Name
Abstract | ||
|---|---|---|
Approximating `real' disease transmission networks through genomic sequence comparisons among pathogenic isolates is increasingly feasible with the current growth in genomic sequence data. Here, we derive a network from over 4,200 globally distributed influenza A virus isolates based on alignment-free sequence comparisons. We then employ network mixing pattern analysis to examine transmission probabilities between isolates from different global regions, host types, subtypes and collection years. While we can not use our results to describe the complete global network of influenza A virus, we present a novel analytical process. In addition, we describe some of the characteristics of this subset of currently available data. Most notable results are the high levels of inter regional links and the important role that avian species seem to play in non human global transmission. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1109/CIBCB.2010.5510336 | Computational Intelligence in Bioinformatics and Computational Biology |
Keywords | Field | DocType |
biology computing,data analysis,genomics,graph theory,probability,alignment-free sequence comparisons,genomic sequence comparisons,global influenza A virus,network mixing pattern analysis,pathogenic isolates,transmission probability | Genome,Transmission (mechanics),Global network,Biology,Mixing patterns,Network topology,Genomics,Bioinformatics,Phylogenetics,Influenza A virus | Conference |
ISBN | Citations | PageRank |
978-1-4244-6766-2 | 0 | 0.34 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Adrienne Breland | 1 | 0 | 1.69 |
| Mehmet Hadi Günes | 2 | 149 | 16.19 |
| Karen Schlauch | 3 | 2 | 2.76 |
| Frederick C. Harris Jr. | 4 | 547 | 78.86 |
| Harris, F.C. | 5 | 4 | 2.34 |