By Topic

Automatic recognition of regions of intrinsically poor multiple alignment using machine learning

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

5 Author(s)
Y. Shan ; Fac. of Comput. Sci., Dalhousie Univ., Halifax, NS, Canada ; E. E. Milios ; A. J. Roger ; C. Blouin
more authors

Phylogenetic analysis requires alignment of gene or protein sequences. Some regions of genes evolve fast and suffer numerous insertion and deletion events and cannot be aligned reliably with automatic alignment algorithms. Such regions of intrinsically uncertain alignment are currently detected and deleted manually before performing phylogenetic analysis. We present the results of a machine learning approach to detect regions of poor alignment automatically. We compare the results obtained from Naive Bayes (NB), C4.5 decision tree (C4.5) and support vector machine (SVM) approaches.

Published in:

Bioinformatics Conference, 2003. CSB 2003. Proceedings of the 2003 IEEE

Date of Conference:

11-14 Aug. 2003