Abstract：
:Regulatory networks provide control over complex cell behavior in all kingdoms of life. Here we describe a statistical model, based on representing proteins as collections of domains or motifs, which predicts unknown molecular interactions within these biological networks. Using known protein-protein interactions of Saccharomyces cerevisiae as training data, we were able to predict the links within this network with only 7% false-negative and 10% false-positive error rates. We also use Markov chain Monte Carlo simulation for the prediction of networks with maximum probability under our model. This model can be applied across species, where interaction data from one (or several) species can be used to infer interactions in another. In addition, the model is extensible and can be analogously applied to other molecular data (e.g., DNA sequences).
journal_name
Geneticsjournal_title
Geneticsauthors
Gomez SM,Lo SH,Rzhetsky Akeywords:
subject
Has Abstractpub_date
2001-11-01 00:00:00pages
1291-8issue
3eissn
0016-6731issn
1943-2631journal_volume
159pub_type
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