Matches in SemOpenAlex for { <https://semopenalex.org/work/W4385993310> ?p ?o ?g. }
- W4385993310 endingPage "99" @default.
- W4385993310 startingPage "89" @default.
- W4385993310 abstract "Safety evaluation of chemicals to human and environmental health is of significance for regulatory decisions. The current high-throughput assays result in a rapid increase of big data for various chemicals, facilitating machine learning (ML)-based quantitative structure–activity relationships (QSAR) modeling. At present, a large number of ML-based prediction models have been innovatively constructed and applied to safety evaluation of various chemicals. Herein, we provide a detailed overview of the development of ML-based QSAR models, especially the model features and relevant algorithms. The databases and predictive schema that can be used for ML-based QSAR modeling are briefly summarized. The current challenges are discussed, and the future developing trends and extended applications of ML-based QSAR modeling in chemical safety evaluation are prospected." @default.
- W4385993310 created "2023-08-19" @default.
- W4385993310 creator A5019477602 @default.
- W4385993310 creator A5028786072 @default.
- W4385993310 creator A5031967712 @default.
- W4385993310 creator A5039495158 @default.
- W4385993310 creator A5046926838 @default.
- W4385993310 creator A5070397169 @default.
- W4385993310 date "2023-01-01" @default.
- W4385993310 modified "2023-10-14" @default.
- W4385993310 title "Machine learning–based QSAR for safety evaluation of environmental chemicals" @default.
- W4385993310 cites W1579194856 @default.
- W4385993310 cites W1663984431 @default.
- W4385993310 cites W1977934535 @default.
- W4385993310 cites W1984811139 @default.
- W4385993310 cites W1986688691 @default.
- W4385993310 cites W1994038869 @default.
- W4385993310 cites W2000089305 @default.
- W4385993310 cites W2003356525 @default.
- W4385993310 cites W2004429971 @default.
- W4385993310 cites W2013191079 @default.
- W4385993310 cites W2017398555 @default.
- W4385993310 cites W2020106098 @default.
- W4385993310 cites W2033757486 @default.
- W4385993310 cites W2040459007 @default.
- W4385993310 cites W2050669291 @default.
- W4385993310 cites W2053369230 @default.
- W4385993310 cites W2107081909 @default.
- W4385993310 cites W2112208320 @default.
- W4385993310 cites W2116774541 @default.
- W4385993310 cites W2128245586 @default.
- W4385993310 cites W2143784594 @default.
- W4385993310 cites W2148950790 @default.
- W4385993310 cites W2317467438 @default.
- W4385993310 cites W2322262466 @default.
- W4385993310 cites W2335463517 @default.
- W4385993310 cites W2434330801 @default.
- W4385993310 cites W2481243666 @default.
- W4385993310 cites W2510245289 @default.
- W4385993310 cites W2563115000 @default.
- W4385993310 cites W2724276442 @default.
- W4385993310 cites W2788633781 @default.
- W4385993310 cites W2799322962 @default.
- W4385993310 cites W2809595040 @default.
- W4385993310 cites W2822962393 @default.
- W4385993310 cites W2893483101 @default.
- W4385993310 cites W2897597313 @default.
- W4385993310 cites W2901476322 @default.
- W4385993310 cites W2930082324 @default.
- W4385993310 cites W2944040221 @default.
- W4385993310 cites W2976546053 @default.
- W4385993310 cites W2982362436 @default.
- W4385993310 cites W2984249386 @default.
- W4385993310 cites W3000877276 @default.
- W4385993310 cites W3003105119 @default.
- W4385993310 cites W3008264426 @default.
- W4385993310 cites W3008572430 @default.
- W4385993310 cites W3012107310 @default.
- W4385993310 cites W3023042104 @default.
- W4385993310 cites W3040137644 @default.
- W4385993310 cites W3126773939 @default.
- W4385993310 cites W3157796038 @default.
- W4385993310 cites W3186265534 @default.
- W4385993310 cites W3196726907 @default.
- W4385993310 cites W3198294617 @default.
- W4385993310 cites W4210538803 @default.
- W4385993310 cites W4235178723 @default.
- W4385993310 cites W4281759084 @default.
- W4385993310 cites W4283704882 @default.
- W4385993310 cites W4294921496 @default.
- W4385993310 doi "https://doi.org/10.1016/b978-0-443-15339-6.00038-2" @default.
- W4385993310 hasPublicationYear "2023" @default.
- W4385993310 type Work @default.
- W4385993310 citedByCount "0" @default.
- W4385993310 crossrefType "book-chapter" @default.
- W4385993310 hasAuthorship W4385993310A5019477602 @default.
- W4385993310 hasAuthorship W4385993310A5028786072 @default.
- W4385993310 hasAuthorship W4385993310A5031967712 @default.
- W4385993310 hasAuthorship W4385993310A5039495158 @default.
- W4385993310 hasAuthorship W4385993310A5046926838 @default.
- W4385993310 hasAuthorship W4385993310A5070397169 @default.
- W4385993310 hasConcept C119857082 @default.
- W4385993310 hasConcept C127413603 @default.
- W4385993310 hasConcept C154945302 @default.
- W4385993310 hasConcept C164126121 @default.
- W4385993310 hasConcept C183696295 @default.
- W4385993310 hasConcept C2987857752 @default.
- W4385993310 hasConcept C41008148 @default.
- W4385993310 hasConcept C52146309 @default.
- W4385993310 hasConcept C71924100 @default.
- W4385993310 hasConcept C99454951 @default.
- W4385993310 hasConceptScore W4385993310C119857082 @default.
- W4385993310 hasConceptScore W4385993310C127413603 @default.
- W4385993310 hasConceptScore W4385993310C154945302 @default.
- W4385993310 hasConceptScore W4385993310C164126121 @default.
- W4385993310 hasConceptScore W4385993310C183696295 @default.
- W4385993310 hasConceptScore W4385993310C2987857752 @default.
- W4385993310 hasConceptScore W4385993310C41008148 @default.