Affordable Access

Weighting features to recognize 3D patterns of electron density in X-ray protein crystallography.

Authors
  • Gopal, Kreshna
  • Romo, Tod D
  • Sacchettini, James C
  • Ioerger, Thomas R
Type
Published Article
Journal
Proceedings / IEEE Computational Systems Bioinformatics Conference, CSB. IEEE Computational Systems Bioinformatics Conference
Publication Date
Jan 01, 2004
Pages
255–265
Identifiers
PMID: 16448019
Source
Medline
License
Unknown

Abstract

Feature selection and weighting are central problems in pattern recognition and instance-based learning. In this work, we discuss the challenges of constructing and weighting features to recognize 3D patterns of electron density to determine protein structures. We present SLIDER, a feature-weighting algorithm that adjusts weights iteratively such that patterns that match query instances are better ranked than mismatching ones. Moreover, SLIDER makes judicious choices of weight values to be considered in each iteration, by examining specific weights at which matching and mismatching patterns switch as nearest neighbors to query instances. This approach reduces the space of weight vectors to be searched. We make the following two main observations: (1) SLIDER efficiently generates weights that contribute significantly in the retrieval of matching electron density patterns; (2) the optimum weight vector is sensitive to the distance metric i.e. feature relevance can be, to a certain extent, sensitive to the underlying metric used to compare patterns.

Report this publication

Statistics

Seen <100 times