Name
Title | ||
|---|---|---|
A GPU-based algorithm for fast node label learning in large and unbalanced biomolecular networks. |
Abstract | ||
|---|---|---|
By parallelizing COSNET we achieved on average a speed-up of 180x in solving the AFP problem in the S. cerevisiae, Mus musculus and Homo sapiens organisms, while lowering memory requirements. In addition, to show the potential applicability of the method to huge biomolecular networks, we predicted node labels in artificially generated sparse networks involving hundreds of thousands to millions of nodes. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1186/s12859-018-2301-4 | BMC Bioinformatics |
Keywords | Field | DocType |
Biological networks,GPU-based Hopfield nets,Large-sized networks,Node label prediction,Protein function prediction | Biology,Gene ontology,Biological network,Prioritization,Computational biology,Genetics,Protein function prediction,DNA microarray,Binary number,Scalability | Journal |
Volume | Issue | ISSN |
19-S | 10 | 1471-2105 |
Citations | PageRank | References |
1 | 0.37 | 18 |
Authors | ||
8 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Marco Frasca | 1 | 38 | 9.72 |
| G. Grossi | 2 | 104 | 21.01 |
| Jessica Gliozzo | 3 | 2 | 2.16 |
| Marco Mesiti | 4 | 830 | 72.53 |
| Marco Notaro | 5 | 10 | 1.96 |
| Paolo Perlasca | 6 | 399 | 18.67 |
| Alessandro Petrini | 7 | 2 | 2.50 |
| Giorgio Valentini | 8 | 905 | 56.70 |