Sivaraman Balakrishnan and Hetunandan Kamisetty contributed equally to this work.
Learning generative models for protein fold families
Version of Record online: 25 JAN 2011
Copyright © 2011 Wiley-Liss, Inc.
Proteins: Structure, Function, and Bioinformatics
Volume 79, Issue 4, pages 1061–1078, April 2011
How to Cite
Balakrishnan, S., Kamisetty, H., Carbonell, J. G., Lee, S.-I. and Langmead, C. J. (2011), Learning generative models for protein fold families. Proteins, 79: 1061–1078. doi: 10.1002/prot.22934
- Issue online: 8 MAR 2011
- Version of Record online: 25 JAN 2011
- Accepted manuscript online: 11 NOV 2010 02:09PM EST
- Manuscript Accepted: 26 OCT 2010
- Manuscript Revised: 10 OCT 2010
- Manuscript Received: 16 AUG 2010
- NSF. Grant Number: IIS-0905193
- Microsoft Research. Use of the OpenCloud cluster for our experiments was generously provided by the Parallel Data Lab at Carnegie Mellon, which is supported, in part, by the NSF, under award CCF-1019104, and the Gordon and Betty Moore Foundation, in the eScience project
Options for accessing this content:
- If you are a society or association member and require assistance with obtaining online access instructions please contact our Journal Customer Services team.
- If your institution does not currently subscribe to this content, please recommend the title to your librarian.
- Login via other institutional login options http://onlinelibrary.wiley.com/login-options.
- You can purchase online access to this Article for a 24-hour period (price varies by title)
- If you already have a Wiley Online Library or Wiley InterScience user account: login above and proceed to purchase the article.
- New Users: Please register, then proceed to purchase the article.
Login via OpenAthens
Search for your institution's name below to login via Shibboleth.
Registered Users please login:
- Access your saved publications, articles and searches
- Manage your email alerts, orders and subscriptions
- Change your contact information, including your password
Please register to:
- Save publications, articles and searches
- Get email alerts
- Get all the benefits mentioned below!