Linked Life Data

10614025

Source:http://linkedlifedata.com/resource/pubmed/id/10614025

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
6
pubmed:dateCreated
2000-1-13
pubmed:abstractText
A back-propagation neural network to predict the carcinogenicity of aromatic nitrogen compounds was developed. The inputs were molecular descriptors of different types: electrostatic, topological, quantum-chemical, physicochemical, etc. For the output the index TD50 as introduced by Gold and colleagues was used, giving a continuous numerical parameter expressing carcinogenicity. From the tens of descriptors calculated, principal component analysis enabled us to restrict the number of parameters to be used for the artificial neural network (ANN). We used 104 molecules for the study. An Rcv2 = 0.69 was obtained. After removal of 12 outliers, a new ANN gave an Rcv2 of 0.82.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:chemical
pubmed:status
MEDLINE
pubmed:issn
0095-2338
pubmed:author
pubmed:issnType
Print
pubmed:volume
39
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
1076-80
pubmed:dateRevised
2006-11-15
pubmed:meshHeading
pubmed:articleTitle
Predictive carcinogenicity: a model for aromatic compounds, with nitrogen-containing substituents, based on molecular descriptors using an artificial neural network.
pubmed:affiliation
Dipartimento di Elettronica e Informazione, Politecnico di Milano, Italy.
pubmed:publicationType
Journal Article, Research Support, Non-U.S. Gov't