Name
Title | ||
|---|---|---|
Contact map prediction using a large-scale ensemble of rule sets and the fusion of multiple predicted structural features. |
Abstract | ||
|---|---|---|
The prediction of a protein's contact map has become in recent years, a crucial stepping stone for the prediction of the complete 3D structure of a protein. In this article, we describe a methodology for this problem that was shown to be successful in CASP8 and CASP9. The methodology is based on (i) the fusion of the prediction of a variety of structural aspects of protein residues, (ii) an ensemble strategy used to facilitate the training process and (iii) a rule-based machine learning system from which we can extract human-readable explanations of the predictor and derive useful information about the contact map representation.The main part of the evaluation is the comparison against the sequence-based contact prediction methods from CASP9, where our method presented the best rank in five out of the six evaluated metrics. We also assess the impact of the size of the ensemble used in our predictor to show the trade-off between performance and training time of our method. Finally, we also study the rule sets generated by our machine learning system. From this analysis, we are able to estimate the contribution of the attributes in our representation and how these interact to derive contact predictions.http://icos.cs.nott.ac.uk/servers/psp.html.natalio.krasnogor@nottingham.ac.ukSupplementary data are available at Bioinformatics online. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1093/bioinformatics/bts472 | Bioinformatics |
Keywords | Field | DocType |
contact map prediction,structural feature,training process,ensemble strategy,uk supplementary information,rule set,large-scale ensemble,contact map representation,protein residue,contact prediction,rule-based machine,derive useful information,sequence-based contact prediction method,training time | Data mining,Computer science,Server,Fusion,Artificial intelligence,Bioinformatics,Machine learning | Journal |
Volume | Issue | ISSN |
28 | 19 | 1367-4811 |
Citations | PageRank | References |
25 | 0.81 | 13 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jaume Bacardit | 1 | 1091 | 47.21 |
| Pawel Widera | 2 | 39 | 3.78 |
| Alfonso Márquez-Chamorro | 3 | 25 | 0.81 |
| Federico Divina | 4 | 249 | 23.99 |
| Jesús S. Aguilar-ruiz | 5 | 625 | 59.56 |
| Natalio Krasnogor | 6 | 1213 | 85.53 |