Cart (Loading....) | Create Account
Close category search window
 

A Markov Chain Monte Carlo Sampling Relevance Vector Machine Model for Recognizing Transcription Start Sites

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)
Huang Juncai ; Coll. of Comput. Sci. & Eng., Univ. of Electron. Sci. & Technol. of China, Chengdu, China ; Wang Fengbi ; Mao Huanzhang ; Zhou mingtian

The task of finding transcription start sites (TSSs) can be modeled as a classification problem. Relevance vector machines (RVM) is a family of machine learning methods that represent a Bayesian approach to the training of general linear models (GLM). Based on the Markov-chain Monte Carlo(MCMC) sampler, propose a model for using the RVM to explore very large numbers of candidate features. The model applyes the power of the RVM to classifying and detecting interesting points and regions in biological sequence data. The model has been used successfully for testing predicting transcription start sites and other features in genome sequences. Our experimental results on real nucleotide sequences data show that our method improve the prediction accuracy greatly and our method performs significantly better than Promoter Inspector and CpG islands.

Published in:

Artificial Intelligence and Computational Intelligence (AICI), 2010 International Conference on  (Volume:3 )

Date of Conference:

23-24 Oct. 2010

Need Help?


IEEE Advancing Technology for Humanity About IEEE Xplore | Contact | Help | Terms of Use | Nondiscrimination Policy | Site Map | Privacy & Opting Out of Cookies

A not-for-profit organization, IEEE is the world's largest professional association for the advancement of technology.
© Copyright 2014 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.