By Topic

Estimating and improving protein interaction error rates

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
P. D'haeseleer ; Lipper Center for Comput. Genetics, Harvard Med. Sch., Boston, MA, USA ; G. M. Church

High throughput protein interaction data sets have proven to be notoriously noisy. Although it is possible to focus on interactions with higher reliability by using only those that are backed up by two or more lines of evidence, this approach invariably throws out the majority of available data. A more optimal use could be achieved by incorporating the probabilities associated with all available interactions into the analysis. We present a novel method for estimating error rates associated with specific protein interaction data sets, as well as with individual interactions given the data sets in which they appear. As a bonus, we also get an estimate for the total number of protein interactions in yeast. Certain types of false positive results can be identified and removed, resulting in a significant improvement in quality of the data set. For copurification data sets, we show how we can reach a tradeoff between the "spoke" and "matrix" representation of interactions within co-purified groups of proteins to achieve an optimal false positive error rate.

Published in:

Computational Systems Bioinformatics Conference, 2004. CSB 2004. Proceedings. 2004 IEEE

Date of Conference:

16-19 Aug. 2004