@article {14508,
	title = {Predicting Protein-Protein Interactions Using Relational Features},
	volume = {CS-TR-4909},
	year = {2007},
	month = {2007/01/07/},
	institution = {Department of Computer Science, University of Maryland, College Park},
	abstract = {Proteins play a fundamental role in ever y process within the cell.Understanding how proteins interact, and the functional units they are
par t of, is important to furthering our knowledge of the entire
biological process. There has been a growing amount of work, both
experimental and computational, on determining the protein-protein
interaction network. Recently researchers have had success looking at
this as a relational learning problem. In this work, we further this
investigation, proposing several novel relational features for
predicting protein-protein interaction. These features can be used in
any classifier. Our approach allows large and complex networks to be
analyzed and is an alternative to using more expensive relational
methods. We show that we are able to get an accuracy of 81.7\% when
predicting new links from noisy high throughput data.
},
	keywords = {Technical Report},
	url = {http://drum.lib.umd.edu/handle/1903/7555},
	author = {Licamele,Louis and Getoor, Lise}
}
