Name
Title | ||
|---|---|---|
Ranking SVM for multiple kernels output combination in protein-protein interaction extraction from biomedical literature |
Abstract | ||
|---|---|---|
Knowledge about protein-protein interactions unveils the molecular mechanisms of biological processes. This paper presents a multiple kernels learning-based approach to automatically extracting protein-protein interactions from biomedical literature. Experimental evaluations show that our approach can achieve state-of-the-art performance with respect to comparable evaluations, with 64.88% F-score and 88.02% area under the receiver operating characteristics curve (AUC) on the AImed corpus. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1109/BIBM.2010.5706635 | BIBM |
Keywords | Field | DocType |
protein-protein interaction extraction,f-score,receiver operating characteristics curve,multiple kernels output combination,learning (artificial intelligence),multiple kernels learning,proteins,support vector machines,ranking svm,molecular mechanisms,biology computing,aimed corpus,protein-protein interaction,biomedical literature,learning,sensitivity analysis,feature extraction,receiver operating characteristic curve,support vector machine,protein protein interaction,data mining,molecular mechanics,f score,protein engineering,kernel,learning artificial intelligence,biological process | Kernel (linear algebra),F1 score,Protein–protein interaction,Receiver operating characteristic,Pattern recognition,Ranking SVM,Protein engineering,Computer science,Support vector machine,Feature extraction,Artificial intelligence,Machine learning | Conference |
ISSN | ISBN | Citations |
2156-1125 | 978-1-4244-8307-5 | 0 |
PageRank | References | Authors |
0.34 | 8 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhihao Yang | 1 | 270 | 36.04 |
| Yuan Lin | 2 | 104 | 16.38 |
| Jiajin Wu | 3 | 16 | 3.93 |
| Nan Tang | 4 | 0 | 0.34 |
| Hongfei Lin | 5 | 768 | 122.52 |
| Yanpeng Li | 6 | 49 | 2.60 |