| Warner G R, Mourikes V E, Neff A M, et al. Mechanisms of action of agrochemicals acting as endocrine disrupting chemicals[J]. Molecular and Cellular Endocrinology, 2020, 502:110680 |
| JakopinŽ. Assessment of the endocrine-disrupting potential of halogenated parabens:An in silico approach[J]. Chemosphere, 2021, 264:128447 |
| Chung C, Park J, Song J E, et al. Determinants of protective behaviors against endocrine disruptors in young Korean women[J]. Asian Nursing Research, 2020, 14(3):165-172 |
| Vieira W T, de Farias M B, Spaolonzi M P, et al. Removal of endocrine disruptors in waters by adsorption, membrane filtration and biodegradation. A review[J]. Environmental Chemistry Letters, 2020, 18(4):1113-1143 |
| 华江环,韩建,郭勇勇,等.孕激素左炔诺孕酮长期低剂量暴露对雄性斑马鱼的生殖毒性[J].生态毒理学报, 2019, 14(2):176-186 Hua J H, Han J, Guo Y Y, et al. Reproductive toxicity in male zebrafish after long-term exposure to low concentrations of progestin levonorgestrel[J]. Asian Journal of Ecotoxicology, 2019, 14(2):176-186(in Chinese) |
| Vieira W T, de Farias M B, Spaolonzi M P, et al. Endocrine-disrupting compounds:Occurrence, detection methods, effects and promising treatment pathways-A critical review[J]. Journal of Environmental Chemical Engineering, 2021, 9(1):104558 |
| Bottalico L N, Weljie A M. Cross-species physiological interactions of endocrine disrupting chemicals with the circadian clock[J]. General and Comparative Endocrinology, 2021, 301:113650 |
| 杨娜,吴航利,王佳,等.农药类内分泌干扰物对动物生殖系统干扰机制的研究进展[J].延安大学学报:自然科学版, 2020, 39(2):87-91 Yang N, Wu H L, Wang J, et al. Research progress on the interference mechanism of pesticide endocrine disrupting chemicals on animal reproductive system[J]. Journal of Yan'an University:Natural Science Edition, 2020, 39(2):87-91(in Chinese) |
| Schilling J, Nepomuceno A I, Planchart A, et al. Machine learning reveals sex-specific 17β[WT《Times New Roman》]-estradiol-responsive expression patterns in white perch (Morone americana) plasma proteins[J]. Proteomics, 2015, 15(15):2678-2690 |
| 蔡德雷,陈江,傅剑云,等.钱塘江水环境内分泌干扰物污染的研究[J].卫生研究, 2011, 40(4):481-484 Cai D L, Chen J, Fu J Y, et al. Study on contamination of endocrine disrupting chemicals in aquatic environment of Qiantang River[J]. Journal of Hygiene Research, 2011, 40(4):481-484(in Chinese) |
| 余方,潘学军,王彬,等.固相萃取-羟基衍生化-气相色谱/质谱联用测定滇池水体中酚类内分泌干扰物[J].环境化学, 2010, 29(4):744-748 Yu F, Pan X J, Wang B, et al. Determination of phenols in surface water of Dianchi Lake by solid extraction-hydroxyl derivatization-GC/MS[J]. Environmental Chemistry, 2010, 29(4):744-748(in Chinese) |
| 张凤仙,胡冠九,郝英群,等.沿海三市饮用水源水内分泌干扰毒性研究[J].生态毒理学报, 2011, 6(3):241-246 Zhang F X, Hu G J, Hao Y Q, et al. Study on endocrine-disrupting toxicity in drinking water sources of three coastal cities[J]. Asian Journal of Ecotoxicology, 2011, 6(3):241-246(in Chinese) |
| 李金荣,郭瑞昕,刘艳华,等.五种典型环境内分泌干扰物赋存及风险评估的研究进展[J].环境化学, 2020, 39(10):2637-2653 Li J R, Guo R X, Liu Y H, et al. Occurrence and risk assessment of five typical environmental endocrine disruptors[J]. Environmental Chemistry, 2020, 39(10):2637-2653(in Chinese) |
| 孟顺龙,宋超,范立民,等.水体中环境内分泌干扰物(EDCs)污染现状及其对鱼类的生殖危害[J].江苏农业学报, 2013, 29(1):202-208 Meng S L, Song C, Fan L M, et al. Pollution of environmental endocrine disrupting chemicals (EDCs) in water and its adverse reproductive effect on fish[J]. Jiangsu Journal of Agricultural Sciences, 2013, 29(1):202-208(in Chinese) |
| McTavish K, Stech H, Stay F. A modeling framework for exploring the population-level effects of endocrine disruptors[J]. Environmental Toxicology and Chemistry, 1998, 17(1):58-67 |
| 黄合田,杨鸿波,孙晓红,等.水产品中内分泌干扰物残留检测方法研究进展[J].水产科学, 2021, 40(2):285-293 Huang H T, Yang H B, Sun X H, et al. Detection methods of environmental endocrine disrupting chemicals in fishery products:Research progress[J]. Fisheries Science, 2021, 40(2):285-293(in Chinese) |
| Jin X W, Zha J M, Xu Y P, et al. Derivation of aquatic predicted no-effect concentration (PNEC) for 2,4-dichlorophenol:Comparing native species data with non-native species data[J]. Chemosphere, 2011, 84(10):1506-1511 |
| Huang Q S, Bu Q W, Zhong W J, et al. Derivation of aquatic predicted no-effect concentration (PNEC) for ibuprofen and sulfamethoxazole based on various toxicity endpoints and the associated risks[J]. Chemosphere, 2018, 193:223-229 |
| Khan K, Roy K, Benfenati E. Ecotoxicological QSAR modeling of endocrine disruptor chemicals[J]. Journal of Hazardous Materials, 2019, 369:707-718 |
| Tinkov O V, Grigorev V Y, Razdolsky A N, et al. Effect of the structural factors of organic compounds on the acute toxicity toward Daphnia magna[J]. SAR and QSAR in Environmental Research, 2020, 31(8):615-641 |
| Fan J T, Yan Z G, Zheng X, et al. Development of inter species correlation estimation (ICE) models to predict the reproduction toxicity of EDCs to aquatic species[J]. Chemosphere, 2019, 224:833-839 |
| Papa E, Villa F, Gramatica P. Statistically validated QSARs, based on theoretical descriptors, for modeling aquatic toxicity of organic chemicals in Pimephales promelas(fathead minnow)[J]. Journal of Chemical Information and Modeling, 2005, 45(5):1256-1266 |
| Mao W F, Song Y, Sui H X, et al. Analysis of individual and combined estrogenic effects of bisphenol, nonylphenol and diethylstilbestrol in immature rats with mathematical models[J]. Environmental Health and Preventive Medicine, 2019, 24(1):32 |
| Yangali-Quintanilla V, Sadmani A, McConville M, et al. A QSAR model for predicting rejection of emerging contaminants (pharmaceuticals, endocrine disruptors) by nanofiltration membranes[J]. Water Research, 2010, 44(2):373-384 |
| Kovarich S, Papa E, Gramatica P. QSAR classification models for the prediction of endocrine disrupting activity of brominated flame retardants[J]. Journal of Hazardous Materials, 2011, 190(1-3):106-112 |
| 张文灏,陈景文,徐童,等.外源化合物在鱼体内生物半减期的QSAR模型[J].生态毒理学报, 2019, 14(3):90-98 Zhang W H, Chen J W, Xu T, et al. QSAR models for predicting biological half-life of xenobiotics in fish[J]. Asian Journal of Ecotoxicology, 2019, 14(3):90-98(in Chinese) |
| 雷太龙.基于机器学习方法的药物毒性的理论预测研究[D].杭州:浙江大学, 2017:1-18 Lei T L. Theoretical prediction of drug toxicity based on machine learning approaches[D]. Hangzhou:Zhejiang University, 2017:1 -18(in Chinese) |
| Cipullo S, Snapir B, Prpich G, et al. Prediction of bioavailability and toxicity of complex chemical mixtures through machine learning models[J]. Chemosphere, 2019, 215:388-395 |
| Roohi R, Jafari M, Jahantab E, et al. Application of artificial neural network model for the identification the effect of municipal waste compost and biochar on phytoremediation of contaminated soils[J]. Journal of Geochemical Exploration, 2020, 208:106399 |
| 薛同来,赵冬晖,韩菲,等. SVR在城市污水BOD预测中的应用[J].新型工业化, 2019, 9(4):94-98 Xue T L, Zhao D H, Han F, et al. Application of support vector regression machine in BOD prediction of urban sewage[J]. The Journal of New Industrialization, 2019, 9(4):94-98(in Chinese) |
| Borrero L A, Guette L S, Lopez E, et al. Predicting toxicity properties through machine learning[J]. Procedia Computer Science, 2020, 170:1011-1016 |
| Caldwell D J, Mastrocco F, Hutchinson T H, et al. Derivation of an aquatic predicted no-effect concentration for the synthetic hormone, 17 alpha-ethinyl estradiol[J]. Environmental Science&Technology, 2008, 42(19):7046-7054 |
| 刘娜,金小伟,王业耀,等.生态毒理数据筛查与评价准则研究[J].生态毒理学报, 2016, 11(3):1-10 Liu N, Jin X W, Wang Y Y, et al. Review of criteria for screening and evaluating ecotoxicity data[J]. Asian Journal of Ecotoxicology, 2016, 11(3):1-10(in Chinese) |
| Sheffield T Y, Judson R S. Ensemble QSAR modeling to predict multi species fish toxicity lethal concentrations and points of departure[J]. Environmental Science&Technology, 2019, 53(21):12793-12802 |
| Yang L, Wang Y H, Chang J, et al. QSAR modeling the toxicity of pesticides against Americamysis bahia[J]. Chemosphere, 2020, 258:127217 |
| Nolte T M, Peijnenburg W J G M, Hendriks A J, et al. Quantitative structure-activity relationships for green algae growth inhibition by polymer particles[J]. Chemosphere, 2017, 179:49-56 |
| Yap C W. PaDEL-descriptor:An open source software to calculate molecular descriptors and fingerprints[J]. Journal of Computational Chemistry, 2011, 32(7):1466-1474 |
| In Y Y, Lee S K, Kim P J, et al. Prediction of acute toxicity to fathead minnow by local model based QSAR and global QSAR approaches[J]. Bulletin of the Korean Chemical Society, 2012, 33(2):613-619 |
| Marzo M, Lavado G J, Como F, et al. QSAR models for biocides:The example of the prediction of Daphnia magna acute toxicity[J]. SAR and QSAR in Environmental Research, 2020, 31(3):227-243 |
| Hossain K A, Roy K. Chemometric modeling of aquatic toxicity of contaminants of emerging concern (CECs) in Dugesia japonica and its inter species correlation with Daphnia and fish:QSTR and QSTTR approaches[J]. Ecotoxicology and Environmental Safety, 2018, 166:92-101 |
| Lei B L, Li J Z, Liu H X, et al. Accurate prediction of aquatic toxicity of aromatic compounds based on genetic algorithm and least squares support vector machines[J]. QSAR&Combinatorial Science, 2008, 27(7):850-865 |
| Niu B, Jin Y H, Lu W C, et al. Predicting toxic action mechanisms of phenols using AdaBoost Learner[J]. Chemometrics and Intelligent Laboratory Systems, 2009, 96(1):43-48 |
| Ai H X, Wu X W, Zhang L, et al. QSAR modelling study of the bioconcentration factor and toxicity of organic compounds to aquatic organisms using machine learning and ensemble methods[J]. Ecotoxicology and Environmental Safety, 2019, 179:71-78 |
| Sun L, Zhang C, Chen Y J, et al. In silico prediction of chemical aquatic toxicity with chemical category approaches and substructural alerts[J]. Toxicology Research, 2015, 4(2):452-463 |
| Pedregosa F, Varoquaux G, Gramfort A, et al. Scikit-learn:Machine learning in python[J]. Journal of Machine Learning Research, 2011, 12:2825-2830 |
| Guo H N, Wu S B, Tian Y J, et al. Application of machine learning methods for the prediction of organic solid waste treatment and recycling processes:A review[J]. Bioresource Technology, 2021, 319:124114 |
| 李云帆.基于机器学习的石化废气污染物排放预测[D].北京:中国石油大学(北京), 2018:7-10 Li Y F. Prediction of petrochemical waste gas emissions based on machine learning[D]. Beijing:China University of Petroleum (Beijing), 2018:7-10(in Chinese) |
| Cheng F X, Shen J, Yu Y, et al. In silico prediction of Tetrahymena pyriformis toxicity for diverse industrial chemicals with substructure pattern recognition and machine learning methods[J]. Chemosphere, 2011, 82(11):1636-1643 |
| Cao Q Q, Liu L, Yang H B, et al. In silico estimation of chemical aquatic toxicity on crustaceans using chemical category methods[J]. Environmental Science Processes&Impacts, 2018, 20(9):1234-1243 |
| OECD. Guidance document on the validation of (quantitative) structure-activity relationship[(Q) SAR] models[R]. Paris:OECD, 2014 |
| Ertürk M D, Saçan M T, Novic M, et al. Quantitative structure-activity relationships (QSARs) using the novel marine algal toxicity data of phenols[J]. Journal of Molecular Graphics&Modelling, 2012, 38:90-100 |
| He L J, Xiao K Y, Zhou C, et al. Insights into pesticide toxicity against aquatic organism:QSTR models on Daphnia magna[J]. Ecotoxicology and Environmental Safety, 2019, 173:285-292 |
| de Morais e Silva L, Lorenzo V P, Lopes W S, et al. Predictive computational tools for assessment of ecotoxicological activity of organic micropollutants in various water sources in Brazil[J]. Molecular Informatics, 2019, 38(8-9):e1800156 |
| 王园宁,刘会会,杨先海.构建有机化合物斑马鱼雌激素干扰效应的二元分类模型[J].生态毒理学报, 2019, 14(4):163-169 Wang Y N, Liu H H, Yang X H. Development of binary classification models for predicting estrogenic activity of organic compounds on zebrafish[J]. Asian Journal of Ecotoxicology, 2019, 14(4):163-169(in Chinese) |
| 米晓希,汤爱涛,朱雨晨,等.机器学习技术在材料科学领域中的应用进展[J].材料导报, 2021, 35(15):15115-15124 Mi X X, Tang A T, Zhu Y C, et al. Research progress of machine learning in material science[J]. Materials Reports, 2021, 35(15):15115-15124(in Chinese) |
| Song I S, Cha J Y, Lee S K. Prediction and analysis of acute fish toxicity of pesticides to the rainbow trout using 2D-QSAR[J]. Analytical Science and Technology, 2011, 24(6):544-555 |
| Grigor'ev V Y, Razdol'skii A N, Zagrebin A O, et al. QSAR classification models of acute toxicity of organic compounds with respect to Daphnia magna[J]. Pharmaceutical Chemistry Journal, 2014, 48(4):242-245 |
| Su Q, Lu W C, Du D S, et al. Prediction of the aquatic toxicity of aromatic compounds to Tetrahymena pyriformis through support vector regression[J]. Oncotarget, 2017, 8(30):49359-49369 |
| Khan K, Khan P M, Lavado G, et al. QSAR modeling of Daphnia magna and fish toxicities of biocides using 2D descriptors[J]. Chemosphere, 2019, 229:8-17 |
| Boone K S, di Toro D M. Target site model:Predicting mode of action and aquatic organism acute toxicity using Abraham parameters and feature-weighted k-nearest neighbors classification[J]. Environmental Toxicology and Chemistry, 2019, 38(2):375-386 |
| Ren S J. Modeling the toxicity of aromatic compounds to Tetrahymena pyriformis:The response surface methodology with nonlinear methods[J]. Journal of Chemical Information and Computer Sciences, 2003, 43(5):1679-1687 |
| Önlü S, Saçan M T. Toxicity of contaminants of emerging concern to Dugesia japonica:QSTR modeling and toxicity relationship with Daphnia magna[J]. Journal of Hazardous Materials, 2018, 351:20-28 |
| Xia B B, Liu K P, Gong Z G, et al. Rapid toxicity prediction of organic chemicals to Chlorella vulgaris using quantitative structure-activity relationships methods[J]. Ecotoxicology and Environmental Safety, 2009, 72(3):787-794 |
| Meng Y B, Lin B L. A feed-forward artificial neural network for prediction of the aquatic ecotoxicity of alcohol ethoxylate[J]. Ecotoxicology and Environmental Safety, 2008, 71(1):172-186 |
| Agatonovic-Kustrin S, Morton D W, Razic S. In silico modelling of pesticide aquatic toxicity[J]. Combinatorial Chemistry&High Throughput Screening, 2014, 17(9):808-818 |
| Polishchuk P G, Muratov E N, Artemenko A G, et al. Application of random forest approach to QSAR prediction of aquatic toxicity[J]. Journal of Chemical Information and Modeling, 2009, 49(11):2481-2488 |
| Habibi-Yangjeh A, Danandeh-Jenagharad M. Application of a genetic algorithm and an artificial neural network for global prediction of the toxicity of phenols to Tetrahymena pyriformis[J]. Chemical Monthly, 2009, 140(11):1279-1288 |
| Louis B, Agrawal V K. QSAR modeling of aquatic toxicity of aromatic aldehydes using artificial neural network (ANN) and multiple linear regression (MLR)[J]. Journal of the Indian Chemical Society, 2011, 88(1):99-107 |
| Sestraş R E, Jäntschi L, Bolboacǎ S D. Poisson parameters of antimicrobial activity:A quantitative structure-activity approach[J]. International Journal of Molecular Sciences, 2012, 13(4):5207-5229 |
| Sangion A, Gramatica P. Hazard of pharmaceuticals for aquatic environment:Prioritization by structural approaches and prediction of ecotoxicity[J]. Environment International, 2016, 95:131-143 |