By Topic

Using Feature Selection Filtering Methods for Binding Site Predictions

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

6 Author(s)
Sun, Yi ; Sci. & Technol. Res. Inst., Hertfordshire Univ., Hatfield ; Robinson, M. ; Adams, R. ; te Boekhorst, R.
more authors

Currently the best algorithms for transcription factor binding site prediction are severely limited in accuracy. In previous work we applied classification techniques on predictions from 12 key prediction algorithms. In this paper, we investigate the classification results when 4 feature selection filtering methods are used. They are bi-normal separation, correlation coefficients, F-score and a cross entropy based algorithm. It is found that all 4 filtering methods perform equally well. Moreover, we show that the worst performing algorithms are not detrimental to the overall performance

Published in:

Cognitive Informatics, 2006. ICCI 2006. 5th IEEE International Conference on  (Volume:1 )

Date of Conference:

17-19 July 2006