This subproject is one of many research subprojects utilizing theresources provided by a Center grant funded by NIH/NCRR. The subproject andinvestigator (PI) may have received primary funding from another NIH source,and thus could be represented in other CRISP entries. The institution listed isfor the Center, which is not necessarily the institution for the investigator.We carried out a large-scale screen to identify interactions between yeast integral membrane proteins using a modified split-ubiquitin technique. Among 705 integral membrane proteins, we identified 1985 putative interactions involving 536 proteins. To ascribe confidence levels to the interactions, we used a support vector machine (SVM) algorithm to classify interactions based on characteristics of the assay results, as well as on protein data derived from the literature. Previously identified and computationally-supported interactions were used to train the SVM, which identified from the complete dataset 131 interactions of highest confidence, 209 of next highest confidence, 468 of next highest, and the remaining 1085 of low confidence.
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