Prediction of peptide bonding affinity: kernel methods for nonlinear modeling

Details Prediction of peptide bonding affinity: kernel methods for nonlinear modeling Prediction of peptide bonding affinity: kernel methods for nonlinear modeling

2011-08-26

arxiv.org/abs/1108.5397v1

Statistics

Machine Learning

Scientific paper

This paper presents regression models obtained from a process of blind prediction of peptide binding affinity from provided descriptors for several distinct datasets as part of the 2006 Comparative Evaluation of Prediction Algorithms (COEPRA) contest. This paper finds that kernel partial least squares, a nonlinear partial least squares (PLS) algorithm, outperforms PLS, and that the incorporation of transferable atom equivalent features improves predictive capability.

Affiliated with

Also associated with

No associations

Rating

Prediction of peptide bonding affinity: kernel methods for nonlinear modeling does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.

If you have personal experience with Prediction of peptide bonding affinity: kernel methods for nonlinear modeling, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Prediction of peptide bonding affinity: kernel methods for nonlinear modeling will most certainly appreciate the feedback.

Rate now

Comments { 0 }

Profile ID: LFWR-SCP-O-613804