Linked Life Data

17044162

Source:http://linkedlifedata.com/resource/pubmed/id/17044162

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
1
pubmed:dateCreated
2006-10-18
pubmed:abstractText
We present a framework for improving local protein alignment algorithms. Specifically, we discuss how to extend local protein aligners to use a collection of vector seeds or ungapped alignment seeds to reduce noise hits. We model picking a set of seed models as an integer programming problem and give algorithms to choose such a set of seeds. While the problem is NP-hard, and Quasi-NP-hard to approximate to within a logarithmic factor, it can be solved easily in practice. A good set of seeds we have chosen allows four to five times fewer false positive hits, while preserving essentially identical sensitivity as BLASTP.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:chemical
pubmed:status
MEDLINE
pubmed:issn
1545-5963
pubmed:author
pubmed:issnType
Print
pubmed:volume
2
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
29-38
pubmed:meshHeading
pubmed:articleTitle
Optimizing multiple seeds for protein homology search.
pubmed:affiliation
School of Computer Science, University of Waterloo, 200 University Ave., West, Waterloo, ON N2L 3G1, Canada. browndg@uwaterloo.ca
pubmed:publicationType
Journal Article, Research Support, Non-U.S. Gov't, Evaluation Studies