System Maintenance:
There may be intermittent impact on performance while updates are in progress. We apologize for the inconvenience.
By Topic

Assigning gene ontology categories (GO) to yeast genes using text-based supervised learning methods

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
$31 $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

4 Author(s)
Izumitani, T. ; NTT Commun. Sci. Labs., Kyoto, Japan ; Taira, H. ; Kazawa, H. ; Maeda, E.

We propose a method for assigning upper level gene ontology terms (GO categories) to genes using relevant documents. This method represents each gene as a vector using relevant documents to the gene. Then, binary classifiers are made for the GO categories using such supervised learning methods as support vector machines and maximum entropy method. We applied this method for assigning GO categories to yeast genes and achieved an average F-measure of 0.67, which is > 0.3 higher than the existing method developed by Raychaudhun et al. We also applied this method to genome-wide annotation for yeast by all GO Slim categories provided by SGD and achieved average F-measures of 0.58, 0.72, and 0.60, respectively, for the three GO parts: cellular component, molecular function, and biological process.

Published in:

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

Date of Conference:

16-19 Aug. 2004