FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W1965431569> ?p ?o ?g. }

• W1965431569 endingPage "475" @default.
• W1965431569 startingPage "465" @default.
• W1965431569 abstract "Robust global models capable of discriminating positive and non-positive carcinogens; and predicting carcinogenic potency of chemicals in rodents were developed. The dataset of 834 structurally diverse chemicals extracted from Carcinogenic Potency Database (CPDB) was used which contained 466 positive and 368 non-positive carcinogens. Twelve non-quantum mechanical molecular descriptors were derived. Structural diversity of the chemicals and nonlinearity in the data were evaluated using Tanimoto similarity index and Brock–Dechert–Scheinkman statistics. Probabilistic neural network (PNN) and generalized regression neural network (GRNN) models were constructed for classification and function optimization problems using the carcinogenicity end point in rat. Validation of the models was performed using the internal and external procedures employing a wide series of statistical checks. PNN constructed using five descriptors rendered classification accuracy of 92.09% in complete rat data. The PNN model rendered classification accuracies of 91.77%, 80.70% and 92.08% in mouse, hamster and pesticide data, respectively. The GRNN constructed with nine descriptors yielded correlation coefficient of 0.896 between the measured and predicted carcinogenic potency with mean squared error (MSE) of 0.44 in complete rat data. The rat carcinogenicity model (GRNN) applied to the mouse and hamster data yielded correlation coefficient and MSE of 0.758, 0.71 and 0.760, 0.46, respectively. The results suggest for wide applicability of the inter-species models in predicting carcinogenic potency of chemicals. Both the PNN and GRNN (inter-species) models constructed here can be useful tools in predicting the carcinogenicity of new chemicals for regulatory purposes." @default.
• W1965431569 created "2016-06-24" @default.
• W1965431569 creator A5014138245 @default.
• W1965431569 creator A5042542566 @default.
• W1965431569 creator A5048702806 @default.
• W1965431569 date "2013-10-01" @default.
• W1965431569 modified "2023-10-09" @default.
• W1965431569 title "Predicting carcinogenicity of diverse chemicals using probabilistic neural network modeling approaches" @default.
• W1965431569 cites W1507259579 @default.
• W1965431569 cites W1965296651 @default.
• W1965431569 cites W1967211424 @default.
• W1965431569 cites W1972758460 @default.
• W1965431569 cites W1979481857 @default.
• W1965431569 cites W1979763885 @default.
• W1965431569 cites W1980646946 @default.
• W1965431569 cites W1983368326 @default.
• W1965431569 cites W1988242038 @default.
• W1965431569 cites W1989906353 @default.
• W1965431569 cites W1990196422 @default.
• W1965431569 cites W1993180192 @default.
• W1965431569 cites W1994946414 @default.
• W1965431569 cites W2000123759 @default.
• W1965431569 cites W2001839857 @default.
• W1965431569 cites W2002405291 @default.
• W1965431569 cites W2006067028 @default.
• W1965431569 cites W2010551096 @default.
• W1965431569 cites W2012995566 @default.
• W1965431569 cites W2015936411 @default.
• W1965431569 cites W2017581448 @default.
• W1965431569 cites W2023308733 @default.
• W1965431569 cites W2023527995 @default.
• W1965431569 cites W2025649629 @default.
• W1965431569 cites W2028720862 @default.
• W1965431569 cites W2031962255 @default.
• W1965431569 cites W2032094685 @default.
• W1965431569 cites W2033501170 @default.
• W1965431569 cites W2040813073 @default.
• W1965431569 cites W2046077065 @default.
• W1965431569 cites W2046596584 @default.
• W1965431569 cites W2047304360 @default.
• W1965431569 cites W2049431230 @default.
• W1965431569 cites W2051271640 @default.
• W1965431569 cites W2053642664 @default.
• W1965431569 cites W2056038686 @default.
• W1965431569 cites W2065256937 @default.
• W1965431569 cites W2065354989 @default.
• W1965431569 cites W2067941404 @default.
• W1965431569 cites W2070528208 @default.
• W1965431569 cites W2071800578 @default.
• W1965431569 cites W2074442200 @default.
• W1965431569 cites W2076871268 @default.
• W1965431569 cites W2079436612 @default.
• W1965431569 cites W2079675288 @default.
• W1965431569 cites W2081930493 @default.
• W1965431569 cites W2091731476 @default.
• W1965431569 cites W2092539345 @default.
• W1965431569 cites W2096729078 @default.
• W1965431569 cites W2116133514 @default.
• W1965431569 cites W2129974599 @default.
• W1965431569 cites W2134473756 @default.
• W1965431569 cites W2142085049 @default.
• W1965431569 cites W2158698691 @default.
• W1965431569 cites W2163646378 @default.
• W1965431569 cites W2176009755 @default.
• W1965431569 cites W2214291252 @default.
• W1965431569 cites W2952798571 @default.
• W1965431569 cites W57233104 @default.
• W1965431569 doi "https://doi.org/10.1016/j.taap.2013.06.029" @default.
• W1965431569 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/23856075" @default.
• W1965431569 hasPublicationYear "2013" @default.
• W1965431569 type Work @default.
• W1965431569 sameAs 1965431569 @default.
• W1965431569 citedByCount "44" @default.
• W1965431569 countsByYear W19654315692014 @default.
• W1965431569 countsByYear W19654315692015 @default.
• W1965431569 countsByYear W19654315692016 @default.
• W1965431569 countsByYear W19654315692017 @default.
• W1965431569 countsByYear W19654315692018 @default.
• W1965431569 countsByYear W19654315692019 @default.
• W1965431569 countsByYear W19654315692020 @default.
• W1965431569 countsByYear W19654315692021 @default.
• W1965431569 countsByYear W19654315692022 @default.
• W1965431569 countsByYear W19654315692023 @default.
• W1965431569 crossrefType "journal-article" @default.
• W1965431569 hasAuthorship W1965431569A5014138245 @default.
• W1965431569 hasAuthorship W1965431569A5042542566 @default.
• W1965431569 hasAuthorship W1965431569A5048702806 @default.
• W1965431569 hasConcept C105795698 @default.
• W1965431569 hasConcept C114246631 @default.
• W1965431569 hasConcept C119857082 @default.
• W1965431569 hasConcept C154945302 @default.
• W1965431569 hasConcept C164126121 @default.
• W1965431569 hasConcept C164923092 @default.
• W1965431569 hasConcept C178790620 @default.
• W1965431569 hasConcept C185592680 @default.
• W1965431569 hasConcept C186060115 @default.
• W1965431569 hasConcept C202751555 @default.
• W1965431569 hasConcept C2780092901 @default.

• next