An Information-Theoretic Definition of Similarity
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
An introduction to ROC analysis
Pattern Recognition Letters - Special issue: ROC analysis in pattern recognition
Information Sciences: an International Journal
Spectral clustering for detecting protein complexes in protein-protein interaction (PPI) networks
Mathematical and Computer Modelling: An International Journal
Detecting protein complexes from noisy protein interaction data
Proceedings of the 11th International Workshop on Data Mining in Bioinformatics
ICICA'12 Proceedings of the Third international conference on Information Computing and Applications
| Hi-index | 0.00 |
Protein interactions and complexes are major components of biological systems. Recent genome-wide applications of tandem affinity purification (TAP) in yeast have increased significantly the available information on such interactions. From these experiments, protein complexes were predicted with different approaches first from the individual experiments only and later from their combination. The resulting predictions showed surprisingly little agreement and all of the corresponding methods rely on additional training data. In this article, we present an unsupervised algorithm for the identification of protein complexes which is independent of the availability of additional complex information. Based on a bootstrap approach, we calculated intuitive confidence scores for interactions which are more accurate than previous scoring metrics. The complexes determined from this confidence network are of similar quality as the complexes identified by the best supervised approaches. Despite the similar quality of the latest predictions and our predictions, considerable differences are still observed between all of them. Nevertheless, the set of consistently identified complexes is more than four times as large as for the first two studies. Our results illustrate that meaningful and reliable complexes can be determined from the purification experiments alone. As a consequence, the approach presented in this article is easily applicable to large-scale TAP experiments for any organism.