Linked Life Data

16108088

Source:http://linkedlifedata.com/resource/pubmed/id/16108088

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
3
pubmed:dateCreated
2005-8-18
pubmed:abstractText
High-throughput computational methods in X-ray protein crystallography are indispensable to meet the goals of structural genomics. In particular, automated interpretation of electron density maps, especially those at mediocre resolution, can significantly speed up the protein structure determination process. TEXTAL(TM) is a software application that uses pattern recognition, case-based reasoning and nearest neighbor learning to produce reasonably refined molecular models, even with average quality data. In this work, we discuss a key issue to enable fast and accurate interpretation of typically noisy electron density data: what features should be used to characterize the density patterns, and how relevant are they? We discuss the challenges of constructing features in this domain, and describe SLIDER, an algorithm to determine the weights of these features. SLIDER searches a space of weights using ranking of matching patterns (relative to mismatching ones) as its evaluation function. Exhaustive search being intractable, SLIDER adopts a greedy approach that judiciously restricts the search space only to weight values that cause the ranking of good matches to change. We show that SLIDER contributes significantly in finding the similarity between density patterns, and discuss the sensitivity of feature relevance to the underlying similarity metric.
pubmed:grant
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:chemical
pubmed:status
MEDLINE
pubmed:month
Jun
pubmed:issn
0219-7200
pubmed:author
pubmed:issnType
Print
pubmed:volume
3
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
645-76
pubmed:dateRevised
2007-11-15
pubmed:meshHeading
pubmed:year
2005
pubmed:articleTitle
Determining relevant features to recognize electron density patterns in x-ray protein crystallography.
pubmed:affiliation
Department of Computer Science, Texas A&M University, 301 H.R. Bright Building, College Station TX 77843-3112, USA. kgopal@cs.tamu.edu
pubmed:publicationType
Journal Article, Research Support, U.S. Gov't, P.H.S., Evaluation Studies, Research Support, N.I.H., Extramural