Linked Life Data

18473737

Source:http://linkedlifedata.com/resource/pubmed/id/18473737

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
4
pubmed:dateCreated
2008-5-13
pubmed:abstractText
This study shows how chemistry knowledge and reasoning are taken into account for building a new methodology that aims at automatically grouping data having a chronological structure. We consider combinatorial catalytic experiments where the evolution of a reaction (e.g., conversion) over time is expected to be analyzed. The mathematical tool has been developed to compare and group curves taking into account their shape. The strategy, which consists on combining a hierarchical clustering with the k-means algorithm, is described and compared with both algorithms used separately. The hybridization is shown to be of great interest. Then, a second application mode of the proposed methodology is presented. Once meaningful clusters according to chemist's preferences and goals are successfully achieved, the induced model may be used in order to automatically classify new experimental results. The grouping of the new catalysts tested for the Heck coupling reaction between styrene and iodobenzene verified the set of criteria "defined" during the initial clustering step, and facilitated a quick identification of the catalytic behaviors following user's preferences.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:chemical
pubmed:status
MEDLINE
pubmed:month
May
pubmed:issn
1386-2073
pubmed:author
pubmed:issnType
Print
pubmed:volume
11
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
266-82
pubmed:meshHeading
pubmed:year
2008
pubmed:articleTitle
Integrating chemists preferences for shape-similarity clustering of series.
pubmed:affiliation
Instituto de Tecnologia Quimica, UPV-CSIC, Valencia, Spain. baumesl@itq.upv.es
pubmed:publicationType
Journal Article, Research Support, Non-U.S. Gov't