Name
Abstract | ||
|---|---|---|
Ranking genes in functional networks according to a specific biological function is a challenging task raising relevant performance and computational complexity problems. To cope with both these problems we developed a transductive gene ranking method based on kernelized score functions able to fully exploit the topology and the graph structure of biomolecular networks and to capture significant functional relationships between genes. We run the method on a network constructed by integrating multiple biomolecular data sources in the yeast model organism, achieving significantly better results than the compared state-of-the-art network-based algorithms for gene function prediction, and with relevant savings in computational time. The proposed approach is general and fast enough to be in perspective applied to other relevant node ranking problems in large and complex biological networks. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1109/TCBB.2012.114 | Computational Biology and Bioinformatics, IEEE/ACM Transactions |
Keywords | Field | DocType |
ranking gene,computational time,biomolecular network,computational complexity problem,biomolecular networks,fast ranking algorithm,relevant saving,relevant node ranking problem,ranking method,complex biological network,predicting gene functions,relevant performance,functional network,topology,computational complexity,bioinformatics,proteins,kernel,prediction algorithms,microorganisms,biological networks,kernel functions,hilbert space,biological function,symmetric matrices,molecular biophysics,genetics | Transduction (machine learning),Computer science,Artificial intelligence,Kernel (linear algebra),Ranking,Biological network,Algorithm,Molecular biophysics,Bioinformatics,Gene regulatory network,Machine learning,Kernel (statistics),Computational complexity theory | Journal |
Volume | Issue | ISSN |
9 | 6 | 1557-9964 |
Citations | PageRank | References |
14 | 0.60 | 15 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Matteo Ré | 1 | 164 | 11.62 |
| Marco Mesiti | 2 | 830 | 72.53 |
| Giorgio Valentini | 3 | 905 | 56.70 |