Finding motif pairs in the interactions between heterogeneous proteins via bootstrapping and boosting

Title
Finding motif pairs in the interactions between heterogeneous proteins via bootstrapping and boosting
Authors
Kim, J.; Huang, D.S.; Han, K.
Keywords
LINEAR MOTIFS, BOOTSTRAPPING
Issue Date
2009-01
Publisher
BIOMED CENTRAL LTD
Abstract
Background: Supervised learning and many stochastic methods for predicting protein-protein interactions require both negative and positive interactions in the training data set. Unlike positive interactions, negative interactions cannot be readily obtained from interaction data, so these must be generated. In protein-protein interactions and other molecular interactions as well, taking all non-positive interactions as negative interactions produces too many negative interactions for the positive interactions. Random selection from non-positive interactions is unsuitable, since the selected data may not reflect the original distribution of data.
Results: We developed a bootstrapping algorithm for generating a negative data set of arbitrary size from protein-protein interaction data. We also developed an efficient boosting algorithm for finding interacting motif pairs in human and virus proteins. The boosting algorithm showed the best performance (84.4% sensitivity and 75.9% specificity) with balanced positive and negative data sets. The boosting algorithm was also used to find potential motif pairs in complexes of human and virus proteins, for which structural data was not used to train the algorithm. Interacting motif pairs common to multiple folds of structural data for the complexes were proven to be statistically significant. The data set for interactions between human and virus proteins was extracted from BOND and is available at http://virus.hpid.org/interactions.aspx. The complexes of human and virus proteins were extracted from PDB and their identifiers are available at http://virus.hpid.org/PDB_IDs. html.

Conclusion: When the positive and negative training data sets are unbalanced, the result via the prediction model tends to be biased. Bootstrapping is effective for generating a negative data set, for which the size and distribution are easily controlled. Our boosting algorithm could efficiently predict interacting motif pairs from protein interaction and sequence data, which was trained with the balanced data sets generated via the bootstrapping method.
URI
http://dspace.inha.ac.kr/handle/10505/1889
ISSN
1471-2105
Appears in Collections:
College of Engineering(공과대학) > Computer Engineering (컴퓨터공학) > Journal Papers, Reports(컴퓨터정보공학 논문, 보고서)
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