Name
Abstract | ||
|---|---|---|
Biological networks are usually used to model interactions among biological macromolecules in a cells. For instance protein-protein interaction networks (PIN) are used to model and analyse the set of interactions among proteins. The comparison of networks may result in the identification of conserved patterns of interactions corresponding to biological relevant entities such as protein complexes and pathways. Several algorithms, known as network alignment algorithms, have been proposed to unravel relations between different species at the interactome level. Algorithms may be categorized in two main classes: merge and mine and mine and merge. Algorithms belonging to the first class initially merge input network into a single integrated and then mine such networks. Conversely algorithms belonging to the second class initially analyze separately two input networks then integrate such results. In this paper we present MODULA (Network Module based PPI Aligner), a novel approach for local network alignment that belong to the second class. The algorithm at first identifies compact modules from input networks. Modules of both networks are then matched using functional knowledge. Then it uses high scoring pairs of modules as seeds to build a bigger alignment. In order to asses MODULA we compared it to the state of the art local alignment algorithms over a rather extensive and updated dataset. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/BIBM.2015.7359918 | IEEE International Conference on Bioinformatics and Biomedicine |
Field | DocType | ISSN |
Data mining,Interactome,Computer science,Theoretical computer science,Artificial intelligence,Local area network,Modula,Merge (version control),Biological network,Interaction network,First class,Smith–Waterman algorithm,Bioinformatics,Machine learning | Conference | 2156-1125 |
Citations | PageRank | References |
1 | 0.35 | 15 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Pietro Hiram Guzzi | 1 | 547 | 65.85 |
| Pierangelo Veltri | 2 | 648 | 82.26 |
| Swarup Roy | 3 | 56 | 12.13 |
| Jugal K. Kalita | 4 | 856 | 62.32 |