Research Article

SIDEpro: A novel machine learning approach for the fast and accurate prediction of side-chain conformations

  1. Ken Nagata1,2,
  2. Arlo Randall1,2,
  3. Pierre Baldi1,2,*

Article first published online: 9 NOV 2011

DOI: 10.1002/prot.23170

Issue

Proteins: Structure, Function, and Bioinformatics

Proteins: Structure, Function, and Bioinformatics

Volume 80, Issue 1, pages 142–153, January 2012

Additional Information

How to Cite

Nagata, K., Randall, A. and Baldi, P. (2012), SIDEpro: A novel machine learning approach for the fast and accurate prediction of side-chain conformations. Proteins, 80: 142–153. doi: 10.1002/prot.23170

Author Information

  1. 1

    Department of Computer Science, University of California, Irvine, California

  2. 2

    Institute for Genomics and Bioinformatics, University of California, Irvine, California

*Department of Computer Science, University of California, Irvine, CA

Publication History

  1. Issue published online: 13 DEC 2011
  2. Article first published online: 9 NOV 2011
  3. Accepted manuscript online: 9 SEP 2011 09:14AM EST
  4. Manuscript Accepted: 31 JUL 2011
  5. Manuscript Revised: 7 JUL 2011
  6. Manuscript Received: 28 FEB 2011

Funded by

  • NIH Biomedical Informatics Training. Grant Number: LM-07443-01
  • NSF MRI. Grant Number: EIA-0321390
  • NSF. Grant Number: IIS-0513376
  • NIH/NLM Pathway to Independence Award. Grant Number: K99 LM010821
  • UCI Institute for Genomics and Bioinformatics

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