Linked Life Data

18693923

Source:http://linkedlifedata.com/resource/pubmed/id/18693923

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:dateCreated
2008-8-12
pubmed:abstractText
Gene annotations with Gene Ontology codes offer scientists important options in their study of genes and their functions. Automatic GO annotation methods have the potential to supplement the intensive manual annotation processes. Annotation approaches using MEDLINE documents are generally two-phased where the first is to annotate documents with GO codes and the second is to annotate gene products via the documents. In this paper we study document annotation with GO codes using a temporal perspective. Specifically, we build adaptive code-specific classifiers. We also study topic drift i.e., changes in the contextual characteristics of annotations over time. We show that topic drift is significant especially in the biological process GO hierarchy. This at least partially explains the particular challenges faced with codes of this hierarchy.
pubmed:commentsCorrections
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:issn
1942-597X
pubmed:author
pubmed:issnType
Electronic
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
681-5
pubmed:dateRevised
2010-9-22
pubmed:meshHeading
pubmed:year
2007
pubmed:articleTitle
Adaptive classifiers, topic drifts and GO annotations.
pubmed:affiliation
School of Library and Information Science, Department of Management Sciences, University of Iowa, USA. padmini-srinivasan@uiowa.edu
pubmed:publicationType
Journal Article, Research Support, U.S. Gov't, Non-P.H.S.