Research graph
References from Machine learning models based on molecular descriptors to predict human and environmental toxicological factors in continental freshwater. Local targets link to admitted publications; unresolved targets remain external evidence.
Impact assessment of a large panel of organic and inorganic micropollutants released by wastewater treatment plants at the scale of France
10.1016/j.watres.2020.116524 · 2021 · External reference
Interpretable random forests via rule extraction
2021 · External reference
VEGA-QSAR: AI Inside a Platform for Predictive Toxicology
2013 · External reference
Categorizing chlordecone potential degradation products to explore their environmental fate
10.1016/j.scitotenv.2016.09.094 · 2017 · External reference
10.1023/a:1010933404324
10.1023/a:1010933404324 · 2001 · External reference
Support-vector networks
10.1007/bf00994018 · 1995 · External reference
Confronting variability with uncertainty in the ecotoxicological impact assessment of down-the-drain products
10.1016/j.envint.2019.01.080 · 2019 · External reference
Support Vector Regression Machines
10.5555/2998981.2999003 · 1997 · External reference
Unresolved reference
2015 · External reference
The New International Standards for Life Cycle Assessment: ISO 14040 and ISO 14044
10.1065/lca2006.02.002 · 2006 · External reference
The Combined QSAR-ICE Models: Practical Application in Ecological Risk Assessment and Water Quality Criteria
10.1021/acs.est.7b02736 · 2017 · External reference
USEtox fate and ecotoxicity factors for comparative assessment of toxic emissions in life cycle analysis: sensitivity to key chemical properties
10.1007/s11367-011-0294-6 · 2011 · External reference
Unresolved reference
2016 · External reference
Estimate ecotoxicity characterization factors for chemicals in life cycle assessment using machine learning models
10.1016/j.envint.2019.105393 · 2020 · External reference
Rapid Prediction of Chemical Ecotoxicity Through Genetic Algorithm Optimized Neural Network Models
10.1021/acssuschemeng.0c03660 · 2020 · External reference
Comparison of locally weighted PLS strategies for regression and discrimination on agronomic NIR data
10.1002/cem.3209 · 2020 · External reference
Classification and Regression by randomForest
2002 · External reference
An integrative machine learning approach for prediction of toxicity-related drug safety
10.26508/lsa.201800098 · 2018 · External reference
Prediction of the Fate of Organic Compounds in the Environment From Their Molecular Properties: A Review
10.1080/10643389.2014.955627 · 2015 · External reference
Assessment of pesticides volatilization potential based on their molecular properties using the TyPol tool
10.1016/j.jhazmat.2021.125613 · 2021 · External reference
Variables Selection for Ecotoxicity and Human Toxicity Characterization Using Gamma Test
10.1007/978-3-319-09150-1_47 · 2014 · External reference
Random Forest for Toxicity of Chemical Emissions: Features Selection and Uncertainty Quantification
10.5890/jeam.2015.09.003 · 2015 · External reference
Unresolved reference
2011 · External reference
Unresolved reference
2019 · External reference
Optimization of classification and regression analysis of four monoclonal antibodies from Raman spectra using collaborative machine learning approach
10.1016/j.talanta.2018.02.109 · 2018 · External reference
10.17226/11970
10.17226/11970 · 2007 · External reference
Unresolved reference
2019 · External reference
mixOmics: An R package for ‘omics feature selection and multiple data integration
10.1371/journal.pcbi.1005752 · 2017 · External reference
A flexible matrix algebra framework for the multimedia multipathway modeling of emission to impacts
10.1016/j.envint.2007.01.004 · 2007 · External reference
USEtox—the UNEP-SETAC toxicity model: recommended characterisation factors for human toxicity and freshwater ecotoxicity in life cycle impact assessment
10.1007/s11367-008-0038-4 · 2008 · External reference
10.2760/300987
10.2760/300987 · 2020 · External reference
TyPol – A new methodology for organic compounds clustering based on their molecular characteristics and environmental behavior
10.1016/j.chemosphere.2014.05.020 · 2014 · External reference
Improved impact assessment of micropollutants release from WWTPs
10.1016/j.cscee.2021.100172 · 2022 · External reference
An Innovative ‘Cluster-then-Predict’ Approach for Improved Sentiment Prediction
10.1007/978-981-10-1023-1_13 · 2016 · External reference
Rapid Life-Cycle Impact Screening Using Artificial Neural Networks
10.1021/acs.est.7b02862 · 2017 · External reference
Accelerating the pace of ecotoxicological assessment using artificial intelligence
10.1007/s13280-021-01598-8 · 2021 · External reference
Identification and characterization of tebuconazole transformation products in soil by combining suspect screening and molecular typology
10.1016/j.envpol.2015.10.027 · 2016 · External reference
Clustering pesticides according to their molecular properties, fate, and effects by considering additional ecotoxicological parameters in the TyPol method
10.1007/s11356-017-0758-8 · 2018 · External reference
Combining cluster analysis with classifier ensembles to predict financial distress
10.1016/j.inffus.2011.12.001 · 2014 · External reference
Unresolved reference
External reference
Unresolved reference
2020 · External reference
LCIA framework and cross-cutting issues guidance within the UNEP-SETAC Life Cycle Initiative
10.1016/j.jclepro.2017.05.206 · 2017 · External reference
Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance
10.3354/cr030079 · 2005 · External reference
Partial least squares,
1985 · External reference
Machine Learning Based Toxicity Prediction: From Chemical Structural Description to Transcriptome Analysis
10.3390/ijms19082358 · 2018 · External reference
Compound Cytotoxicity Profiling Using Quantitative High-Throughput Screening
10.1289/ehp.10727 · 2008 · External reference
