FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W2034706201> ?p ?o ?g. }

• W2034706201 endingPage "294" @default.
• W2034706201 startingPage "277" @default.
• W2034706201 abstract "An in silico method for predicting percutaneous absorption of cosmetic ingredients was developed by using artificial neural network (ANN) analysis to predict the human skin permeability coefficient (log Kp), taking account of the physicochemical properties of the vehicle, and the apparent diffusion coefficient (log D). Molecular weight and octanol-water partition coefficient (log P) of chemicals, and log P of the vehicles, were used as molecular descriptors for predicting log Kp and log D of 359 samples, for which literature values of either or both of log Kp and log D were available. Adaptivity of the ANN model was evaluated in comparison with a multiple linear regression model (MLR) by calculating the root-mean-square (RMS) errors. Accuracy and robustness were confirmed by 10-fold cross-validation. The predictive RMS errors of the ANN model were smaller than those of the MLR model (log Kp; 0.675 vs 0.887, log D; 0.553 vs 0.658), indicating superior performance. The predictive RMS errors for log Kp and log D with the ANN model after 10-fold cross-validation analysis were 0.723 and 0.606, respectively. Moreover, we estimated the cumulative amounts of chemicals permeated into the skin during 24 hr (Q24hr) from the values of log Kp and log D by applying Fick’s law of diffusion. Our results suggest that this newly established ANN analysis method, taking account of the property of the vehicle, could contribute to non-animal risk assessment of cosmetic ingredients by providing a tool for calculating Q24hr, which is required for evaluating the margin of safety." @default.
• W2034706201 created "2016-06-24" @default.
• W2034706201 creator A5043626530 @default.
• W2034706201 creator A5048549244 @default.
• W2034706201 creator A5077998037 @default.
• W2034706201 creator A5082694156 @default.
• W2034706201 creator A5091285288 @default.
• W2034706201 date "2015-01-01" @default.
• W2034706201 modified "2023-10-18" @default.
• W2034706201 title "Artificial neural network analysis for predicting human percutaneous absorption taking account of vehicle properties" @default.
• W2034706201 cites W112176633 @default.
• W2034706201 cites W1501864094 @default.
• W2034706201 cites W1517270983 @default.
• W2034706201 cites W1520834681 @default.
• W2034706201 cites W1561210178 @default.
• W2034706201 cites W1568499110 @default.
• W2034706201 cites W163194334 @default.
• W2034706201 cites W1965336467 @default.
• W2034706201 cites W1971056306 @default.
• W2034706201 cites W1971643336 @default.
• W2034706201 cites W1975067418 @default.
• W2034706201 cites W1976145832 @default.
• W2034706201 cites W1976372018 @default.
• W2034706201 cites W1978060458 @default.
• W2034706201 cites W1979927975 @default.
• W2034706201 cites W1980398411 @default.
• W2034706201 cites W1991863274 @default.
• W2034706201 cites W1993402424 @default.
• W2034706201 cites W1996908687 @default.
• W2034706201 cites W1998840708 @default.
• W2034706201 cites W1999610960 @default.
• W2034706201 cites W1999943077 @default.
• W2034706201 cites W2002263748 @default.
• W2034706201 cites W2009994635 @default.
• W2034706201 cites W2014227937 @default.
• W2034706201 cites W2019344710 @default.
• W2034706201 cites W2019440756 @default.
• W2034706201 cites W2021783581 @default.
• W2034706201 cites W2031627294 @default.
• W2034706201 cites W2034053988 @default.
• W2034706201 cites W2034314699 @default.
• W2034706201 cites W2036433758 @default.
• W2034706201 cites W2042317104 @default.
• W2034706201 cites W2043824503 @default.
• W2034706201 cites W2044078572 @default.
• W2034706201 cites W2045460917 @default.
• W2034706201 cites W2045946444 @default.
• W2034706201 cites W2047312353 @default.
• W2034706201 cites W2048058583 @default.
• W2034706201 cites W2048337884 @default.
• W2034706201 cites W2048694743 @default.
• W2034706201 cites W2055532901 @default.
• W2034706201 cites W2056217859 @default.
• W2034706201 cites W2063977869 @default.
• W2034706201 cites W2072369531 @default.
• W2034706201 cites W2075145360 @default.
• W2034706201 cites W2084830833 @default.
• W2034706201 cites W2086364016 @default.
• W2034706201 cites W2087020647 @default.
• W2034706201 cites W2087879100 @default.
• W2034706201 cites W2089621094 @default.
• W2034706201 cites W2089855787 @default.
• W2034706201 cites W2091247610 @default.
• W2034706201 cites W2091302426 @default.
• W2034706201 cites W2091831115 @default.
• W2034706201 cites W2098874108 @default.
• W2034706201 cites W2101453828 @default.
• W2034706201 cites W2109744806 @default.
• W2034706201 cites W2111476410 @default.
• W2034706201 cites W2118224461 @default.
• W2034706201 cites W2118984839 @default.
• W2034706201 cites W2121672336 @default.
• W2034706201 cites W2124111070 @default.
• W2034706201 cites W2128104791 @default.
• W2034706201 cites W2128404895 @default.
• W2034706201 cites W2138492659 @default.
• W2034706201 cites W2141095211 @default.
• W2034706201 cites W2151702186 @default.
• W2034706201 cites W2172187417 @default.
• W2034706201 cites W2199498392 @default.
• W2034706201 cites W3021056057 @default.
• W2034706201 cites W316775343 @default.
• W2034706201 cites W35738354 @default.
• W2034706201 cites W4231475497 @default.
• W2034706201 cites W4232103952 @default.
• W2034706201 cites W4317675750 @default.
• W2034706201 cites W93096023 @default.
• W2034706201 doi "https://doi.org/10.2131/jts.40.277" @default.
• W2034706201 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/25786531" @default.
• W2034706201 hasPublicationYear "2015" @default.
• W2034706201 type Work @default.
• W2034706201 sameAs 2034706201 @default.
• W2034706201 citedByCount "16" @default.
• W2034706201 countsByYear W20347062012016 @default.
• W2034706201 countsByYear W20347062012017 @default.
• W2034706201 countsByYear W20347062012018 @default.
• W2034706201 countsByYear W20347062012019 @default.
• W2034706201 countsByYear W20347062012020 @default.

• next