Abstract:
:Most proteins execute their functions through interacting with other proteins. Thus, understanding protein-protein interactions (PPIs) is essential to decipher biological functions in a living cell. To predict large-scale PPIs, effective and efficient computational approaches are desirable to integrate heterogeneous data sources provided by advanced technologies. In this paper, we extend our previous work on a Bayesian classifier for human PPI predictions from model organisms, by introducing a tree-augmented naïve Bayes (TAN) classifier. TAN maintains the simplicity and robustness of a naïve Bayes classifier while allows for the dependence among variables. Our empirical results show that by integrating features extracted from microarray expression measurements, Gene Ontology values, and orthologous scores, TAN achieves higher classification accuracy than the manually constructed Bayesian network classifier and naïve Bayes. For human PPI prediction, TAN obtains 88% sensitivity while keeping a reasonable 70% specificity on testing samples.
journal_name
Proteomicsjournal_title
Proteomicsauthors
Lin X,Chen XWdoi
10.1002/pmic.201200326subject
Has Abstractpub_date
2013-01-01 00:00:00pages
261-8issue
2eissn
1615-9853issn
1615-9861journal_volume
13pub_type
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