Linked Life Data

19768214

Source:http://linkedlifedata.com/resource/pubmed/id/19768214

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
10
pubmed:dateCreated
2009-9-21
pubmed:abstractText
The first reported hybrid artificial neural network-genetic algorithm (ANN-GA) approach for the optimization of on-capillary dipeptide derivatization is presented. More specifically, genetic optimization proved valuable in the determination of effective network structure with three defined parameter inputs: (i) phthalic anhydride injection volume, (ii) time of injection, and (iii) voltage, for the maximum conversion of the dipeptide D-Ala-D-Ala by phthalic anhydride. Results obtained from the hybrid approach proved superior to an ANN model without GA optimization in terms of training data and predictive ability. The model developed will likely prove useful for the analysis of other organic-based reaction systems.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:chemical
pubmed:status
MEDLINE
pubmed:month
Oct
pubmed:issn
1364-5528
pubmed:author
pubmed:issnType
Electronic
pubmed:volume
134
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
2067-70
pubmed:meshHeading
pubmed:year
2009
pubmed:articleTitle
On-capillary derivatization using a hybrid artificial neural network-genetic algorithm approach.
pubmed:affiliation
Department of Chemistry and Biochemistry, California State University, Los Angeles, 5151 State University Drive, Los Angeles, CA 90032, USA.
pubmed:publicationType
Journal Article, Research Support, U.S. Gov't, Non-P.H.S., Research Support, Non-U.S. Gov't