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Hirokazu Kouzuki

Showing results (1-10 of 20) with videos related to

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Journal of Applied Toxicology : JAT|December 12, 2017
Development of an artificial neural network model for risk assessment of skin sensitization using human cell line activation test, direct peptide reactivity assay, KeratinoSens™ and in silico structure alert parameterMorihiko Hirota, Takao Ashikaga, Hirokazu Kouzuki
The Journal of Toxicological Sciences|March 7, 2015
Prediction of genotoxic potential of cosmetic ingredients by an in silico battery system consisting of a combination of an expert rule-based system and a statistics-based systemMaki Aiba née Kaneko, Morihiko Hirota, Hirokazu Kouzuki, et al.
The Journal of Toxicological Sciences|March 20, 2015
Artificial neural network analysis for predicting human percutaneous absorption taking account of vehicle propertiesTomomi Atobe, Masaaki Mori, Fumiyoshi Yamashita, et al.
The Journal of Toxicological Sciences|March 20, 2015
In silico risk assessment for skin sensitization using artificial neural network analysisKyoko Tsujita-Inoue, Tomomi Atobe, Morihiko Hirota, et al.
The Journal of Toxicological Sciences|February 17, 2020
Integration of read-across and artificial neural network-based QSAR models for predicting systemic toxicity: A case study for valproic acidTomoka Hisaki, Maki Aiba Née Kaneko, Morihiko Hirota, et al.
The Journal of Toxicological Sciences|March 20, 2015
Development of QSAR models using artificial neural network analysis for risk assessment of repeated-dose, reproductive, and developmental toxicities of cosmetic ingredientsTomoka Hisaki, Maki Aiba Née Kaneko, Masahiko Yamaguchi, et al.
Journal of Applied Toxicology : JAT|October 30, 2015
Development of novel in vitro photosafety assays focused on the Keap1-Nrf2-ARE pathwayKyoko Tsujita-Inoue, Morihiko Hirota, Tomomi Atobe, et al.
Toxicology in Vitro : an International Journal Published in Association with BIBRA|January 22, 2014
Skin sensitization risk assessment model using artificial neural network analysis of data from multiple in vitro assaysKyoko Tsujita-Inoue, Morihiko Hirota, Takao Ashikaga, et al.
Toxicology in Vitro : an International Journal Published in Association with BIBRA|March 6, 2013
Artificial neural network analysis of data from multiple in vitro assays for prediction of skin sensitization potency of chemicalsMorihiko Hirota, Hirokazu Kouzuki, Takao Ashikaga, et al.
Toxicology in Vitro : an International Journal Published in Association with BIBRA|September 12, 2021
In chemico sequential testing strategy for assessing the photoallegic potentialHayato Nishida, Toshiyuki Ohtake, Takao Ashikaga, et al.
Pageof 2

Showing results (1-10 of 20) with videos related to

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Pageof 2
Journal of Applied Toxicology : JAT|December 12, 2017
Development of an artificial neural network model for risk assessment of skin sensitization using human cell line activation test, direct peptide reactivity assay, KeratinoSens™ and in silico structure alert parameterMorihiko Hirota, Takao Ashikaga, Hirokazu Kouzuki
The Journal of Toxicological Sciences|March 7, 2015
Prediction of genotoxic potential of cosmetic ingredients by an in silico battery system consisting of a combination of an expert rule-based system and a statistics-based systemMaki Aiba née Kaneko, Morihiko Hirota, Hirokazu Kouzuki, et al.
The Journal of Toxicological Sciences|March 20, 2015
Artificial neural network analysis for predicting human percutaneous absorption taking account of vehicle propertiesTomomi Atobe, Masaaki Mori, Fumiyoshi Yamashita, et al.
The Journal of Toxicological Sciences|March 20, 2015
In silico risk assessment for skin sensitization using artificial neural network analysisKyoko Tsujita-Inoue, Tomomi Atobe, Morihiko Hirota, et al.
The Journal of Toxicological Sciences|February 17, 2020
Integration of read-across and artificial neural network-based QSAR models for predicting systemic toxicity: A case study for valproic acidTomoka Hisaki, Maki Aiba Née Kaneko, Morihiko Hirota, et al.
The Journal of Toxicological Sciences|March 20, 2015
Development of QSAR models using artificial neural network analysis for risk assessment of repeated-dose, reproductive, and developmental toxicities of cosmetic ingredientsTomoka Hisaki, Maki Aiba Née Kaneko, Masahiko Yamaguchi, et al.
Journal of Applied Toxicology : JAT|October 30, 2015
Development of novel in vitro photosafety assays focused on the Keap1-Nrf2-ARE pathwayKyoko Tsujita-Inoue, Morihiko Hirota, Tomomi Atobe, et al.
Toxicology in Vitro : an International Journal Published in Association with BIBRA|January 22, 2014
Skin sensitization risk assessment model using artificial neural network analysis of data from multiple in vitro assaysKyoko Tsujita-Inoue, Morihiko Hirota, Takao Ashikaga, et al.
Toxicology in Vitro : an International Journal Published in Association with BIBRA|March 6, 2013
Artificial neural network analysis of data from multiple in vitro assays for prediction of skin sensitization potency of chemicalsMorihiko Hirota, Hirokazu Kouzuki, Takao Ashikaga, et al.
Toxicology in Vitro : an International Journal Published in Association with BIBRA|September 12, 2021
In chemico sequential testing strategy for assessing the photoallegic potentialHayato Nishida, Toshiyuki Ohtake, Takao Ashikaga, et al.
Pageof 2