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ED Profiler: Machine Learning Tool for Screening Potential Endocrine-Disrupting Chemicals

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Machine Learning and Deep Learning in Computational Toxicology

Part of the book series: Computational Methods in Engineering & the Sciences ((CMES))

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Abstract

Endocrine-disrupting chemicals (EDCs) could evoke untold endocrine-related detrimental effects on humans and wildlife. To minimize the potential deleterious effects of EDCs on the endocrine system of living organisms, we should identify and screen potential EDCs from the current myriad of commercially used chemicals. Computational models and software have been increasingly recognized as a valuable, effective, and powerful high-throughput virtual screening tool that could be employed to screen potential EDCs. To date, the number of available predictive models and software for nonreceptor-mediated targets were less than that of nuclear receptors. Importantly, tools with predictive models for hormone transport proteins (one critical nonreceptor-mediated target) were scarce. Thus, it is a driving imperative to develop more models related to nonreceptor-mediated targets and to deploy tools capable of nonreceptor-mediated target modeling. In this chapter, we introduce a high-throughput virtual screening tool named “ED Profiler,” which has been integrated with (quantitative) structure–activity relationship ((Q)SAR) models for some nonreceptor-mediated targets (e.g. human and fish hormone transport proteins) and could be used to predict the potential disrupting effects of EDCs on nonreceptor-mediated targets. The (Q)SAR models were derived using typical machine learning algorithms, i.e. k-nearest neighbor (kNN) or decision tree. The ED Profiler was developed and deployed in Python. Leveraging the power of ED Profiler, we could categorize whether a given substance within the applicability domain of corresponding (Q)SAR) models was a potential nonreceptor-mediated target disruptor or not.

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References

  • AbdulHameed MDM, Liu RF, Schyman P, Sachs D, Xu Z, Desai V, Wallqvist A (2021) ToxProfiler: Toxicity-target profiler based on chemical similarity. Comput Toxicol 18:100162

    Article  CAS  Google Scholar 

  • Alsen M, Sinclair C, Cooke P, Ziadkhanpour K, Genden E, van Gerwen M (2021) Endocrine disrupting chemicals and thyroid cancer: an overview. Toxics 9(1):14

    Article  CAS  Google Scholar 

  • Banerjee P, Eckert AO, Schrey AK, Preissner R (2018) ProTox-II: a webserver for the prediction of toxicity of chemicals. Nucleic Acids Res 46(W1):W257–W263

    Article  CAS  Google Scholar 

  • Browne P, Judson RS, Casey WM, Kleinstreuer NC, Thomas RS (2015) Screening chemicals for estrogen receptor bioactivity using a computational model. Environ Sci Technol 49(14):8804–8814

    Article  CAS  Google Scholar 

  • Browne P, Noyes PD, Casey WM, Dix DJ (2017) Application of adverse outcome pathways to U.S. EPA’s endocrine disruptor screening program. Environ Health Perspect 125(9):096001

    Google Scholar 

  • Browne P, Van Der Wal L, Gourmelon A (2020) OECD approaches and considerations for regulatory evaluation of endocrine disruptors. Mol Cell Endocrinol 504:110675

    Article  CAS  Google Scholar 

  • Bokobza E, Hinault C, Tiroille V, Clavel S, Bost F, Chevalier N (2021) The adipose tissue at the crosstalk between EDCs and cancer development. Front Endocrinol (lausanne) 12:691658

    Article  Google Scholar 

  • Buckalew AR, Wang J, Murr AS, Deisenroth C, Stewart WM, Stoker TE, Laws SC (2020) Evaluation of potential sodium-iodide symporter (NIS) inhibitors using a secondary Fischer rat thyroid follicular cell (FRTL-5) radioactive iodide uptake (RAIU) assay. Arch Toxicol 94(3):873–885

    Article  CAS  Google Scholar 

  • Chen Q, Tan H, Yu H, Shi W (2018) Activation of steroid hormone receptors: shed light on the in silico evaluation of endocrine disrupting chemicals. Sci Total Environ 631–632:27–39

    Google Scholar 

  • Chierici M, Giulini M, Bussola N, Jurman G, Furlanello C (2018) Machine learning models for predicting endocrine disruption potential of environmental chemicals. J Environ Sci Health C Environ Carcinog Ecotoxicol Rev 36(4):237–251

    Article  CAS  Google Scholar 

  • Chirico N, Gramatica P (2012) Real external predictivity of QSAR models. Part 2. New intercomparable thresholds for different validation criteria and the need for scatter plot inspection. J Chem Inf Model 52(8):2044–2058

    Google Scholar 

  • Colborn T, vom Saal FS, Soto AM (1993) Developmental effects of endocrine-disrupting chemicals in wildlife and humans. Environ Health Perspect 101(5):378–384

    Article  CAS  Google Scholar 

  • Diamanti-Kandarakis E, Bourguignon JP, Giudice LC, Hauser R, Prins GS, Soto AM, Zoeller RT, Gore AC (2009) Endocrine-disrupting chemicals: an endocrine society scientific statement. Endocr Rev 30(4):293–342

    Article  CAS  Google Scholar 

  • Ding F, Wang Z, Yang XH, Shi LL, Liu JN, Chen GS (2019) Development of classification models for predicting chronic toxicity of chemicals to Daphnia magna and Pseudokirchneriella subcapitata. SAR QSAR Environ Res 30(1):39–50

    Article  CAS  Google Scholar 

  • Dimitrov SD, Diderich R, Sobanski T, Pavlov TS, Chankov GV, Chapkanov AS, Karakolev YH, Temelkov SG, Vasilev RA, Gerova KD, Kuseva CD, Todorova ND, Mehmed AM, Rasenberg M, Mekenyan OG (2016) QSAR Toolbox—workflow and major functionalities. SAR QSAR Environ Res 27(3):203–219

    Article  CAS  Google Scholar 

  • Hallinger DR, Murr AS, Buckalew AR, Simmons SO, Stoker TE, Laws SC (2017) Development of a screening approach to detect thyroid disrupting chemicals that inhibit the human sodium iodide symporter (NIS). Toxicol in Vitro 40:66–78

    Article  CAS  Google Scholar 

  • Harris CA, Hamilton PB, Runnalls TJ, Vinciotti V, Henshaw A, Hodgson D, Coe TS, Jobling S, Tyler CR, Sumpter JP (2011) The consequences of feminization in breeding groups of wild fish. Environ Health Perspect 119:306–311

    Article  Google Scholar 

  • He JY, Peng T, Yang XH, Liu HH (2018) Development of QSAR models for predicting the binding affinity of endocrine disrupting chemicals to eight fish estrogen receptor. Ecotoxicol Environ Saf 148:211–219

    Article  CAS  Google Scholar 

  • Hong H, Branham WS, Ng HW, Moland CL, Dial SL, Fang H, Perkins R, Sheehan D, Tong W (2015) Human sex hormone-binding globulin binding affinities of 125 structurally diverse chemicals and comparison with their binding to androgen receptor, estrogen receptor, and α-fetoprotein. Toxicol Sci 143(2):333–348

    Article  CAS  Google Scholar 

  • Huang K, Wang X, Zhang H, Zeng L, Zhang X, Wang B, Zhou Y, Jing T (2020) Structure-directed screening and analysis of thyroid-disrupting chemicals targeting transthyretin based on molecular recognition and chromatographic separation. Environ Sci Technol 54(9):5437–5445

    Article  CAS  Google Scholar 

  • Gimeno S, Gerritsen A, Bowmer T, Komen H (1996) Feminization of male carp. Nature 384:221–222

    Article  CAS  Google Scholar 

  • Gore AC, Chappell VA, Fenton SE, Flaws JA, Nadal A, Prins GS, Toppari J, Zoeller RT (2015) EDC-2: the endocrine society’s second scientific statement on endocrine-disrupting chemicals. Endocr Rev 36(6):E1–E150

    Article  CAS  Google Scholar 

  • Garcia de Lomana M, Morger A, Norinder U, Buesen R, Landsiedel R, Volkamer A, Kirchmair J, Mathea M (2021) ChemBioSim: enhancing conformal prediction of in vivo toxicity by use of predicted bioactivities. J Chem Inf Model 61(7):3255–3272

    Article  CAS  Google Scholar 

  • Guo J, Shi W, Chen Q, Deng D, Zhang X, Wei S, Yu N, Giesy JP, Yu H (2017) Extended virtual screening strategies to link antiandrogenic activities and detected organic contaminants in soils. Environ Sci Technol 51(21):12528–12536

    Article  CAS  Google Scholar 

  • Idakwo G, Luttrell J, Chen M, Hong H, Zhou Z, Gong P, Zhang C (2018) A review on machine learning methods for in silico toxicity prediction. J Environ Sci Health C Environ Carcinog Ecotoxicol Rev 36(4):169–191

    Article  CAS  Google Scholar 

  • Kassotis CD, Vandenberg LN, Demeneix BA, Porta M, Slama R, Trasande L (2020) Endocrine-disrupting chemicals: economic, regulatory, and policy implications. Lancet Diabetes Endocrinol 8(8):719–730

    Article  CAS  Google Scholar 

  • Kim S, Choi K, Ji K, Seo J, Kho Y, Park J, Kim S, Park S, Hwang I, Jeon J, Yang H, Giesy JP (2011) Trans-placental transfer of thirteen perfluorinated compounds and relations with fetal thyroid hormones. Environ Sci Technol 45(17):7465–7472

    Article  CAS  Google Scholar 

  • Kolšek K, Mavri J, Sollner Dolenc M, Gobec S, Turk S (2014) Endocrine disruptome–an open source prediction tool for assessing endocrine disruption potential through nuclear receptor binding. J Chem Inf Model 54(4):1254–1267

    Article  Google Scholar 

  • Kwiatkowski CF, Bolden AL, Liroff RA, Rochester JR, Vandenbergh JG (2016) Twenty-Five Years of endocrine disruption science: remembering Theo Colborn. Environ Health Perspect 124(9):A151–A154

    Article  Google Scholar 

  • La Merrill MA, Vandenberg LN, Smith MT, Goodson W, Browne P, Patisaul HB, Guyton KZ, Kortenkamp A, Cogliano VJ, Woodruff TJ, Rieswijk L, Sone H, Korach KS, Gore AC, Zeise L, Zoeller RT (2020) Consensus on the key characteristics of endocrine-disrupting chemicals as a basis for hazard identification. Nat Rev Endocrinol 16(1):45–57

    Article  Google Scholar 

  • LeBaron MJ, Coady KK, O'Connor JC, Nabb DL, Markell LK, Snajdr S, Sue Marty M (2014) Key learnings from performance of the U.S. EPA endocrine disruptor screening program (EDSP) Tier 1 in vitro assays. Birth Defects Res B Dev Reprod Toxicol 101(1): 23–42

    Google Scholar 

  • Li F, Xie Q, Li X, Li N, Chi P, Chen J, Wang Z, Hao C (2010) Hormone activity of hydroxylated polybrominated diphenyl ethers on human thyroid receptor-beta: in vitro and in silico investigations. Environ Health Perspect 118(5):602–606

    Article  CAS  Google Scholar 

  • Lin SY, Yang XH, Liu HH (2019) Development of liposome/water partition coefficients predictive models for neutral and ionogenic organic chemicals. Ecotox Environ Safe 179: 40–49

    Article  CAS  Google Scholar 

  • Liu HH, Yang XH, Lu R (2016) Development of classification model and QSAR model for predicting binding affinity of endocrine disrupting chemicals to human sex hormone-binding globulin. Chemosphere 156:1–7

    Article  CAS  Google Scholar 

  • Liu HH, Yang XH, Yin C, Wei MB, He X (2017) Development of predictive models for predicting binding affinity of endocrine disrupting chemicals to fish sex hormone-binding globulin. Ecotoxicol Environ Saf 136:46–54

    Article  CAS  Google Scholar 

  • Lu L, Zhan T, Ma M, Xu C, Wang J, Zhang C, Liu W, Zhuang S (2018) Thyroid disruption by bisphenol s analogues via thyroid hormone receptor β: in vitro, in vivo, and molecular dynamics simulation study. Environ Sci Technol 52(11):6617–6625

    Article  CAS  Google Scholar 

  • Lu L, Wu H, Cui S, Zhan T, Zhang C, Lu S, Liu W, Zhuang S (2020) Pentabromoethylbenzene exposure induces transcriptome aberration and thyroid dysfunction: in vitro, in silico, and in vivo investigations. Environ Sci Technol 54(19):12335–12344

    Article  CAS  Google Scholar 

  • Mansouri K, Kleinstreuer N, Abdelaziz AM, Alberga D, Alves VM, Andersson PL, Andrade CH, Bai F, Balabin I, Ballabio D, Benfenati E, Bhhatarai B, Boyer S, Chen J, Consonni V, Farag S, Fourches D, García-Sosa AT, Gramatica P, Grisoni F, Grulke CM, Hong H, Horvath D, Hu X, Huang R, Jeliazkova N, Li J, Li X, Liu H, Manganelli S, Mangiatordi GF, Maran U, Marcou G, Martin T, Muratov E, Nguyen DT, Nicolotti O, Nikolov NG, Norinder U, Papa E, Petitjean M, Piir G, Pogodin P, Poroikov V, Qiao X, Richard AM, Roncaglioni A, Ruiz P, Rupakheti C, Sakkiah S, Sangion A, Schramm KW, Selvaraj C, Shah I, Sild S, Sun L, Taboureau O, Tang Y, Tetko IV, Todeschini R, Tong W, Trisciuzzi D, Tropsha A, Van Den Driessche G, Varnek A, Wang Z, Wedebye EB, Williams AJ, Xie H, Zakharov AV, Zheng Z, Judson RS (2020) CoMPARA: collaborative modeling project for androgen receptor activity. Environ Health Perspect 128(2):27002

    Article  CAS  Google Scholar 

  • Marty S (2014) Introduction to screening for endocrine activity-experiences with the US EPA’s endocrine disruptor screening program and future considerations. Birth Defects Res B Dev Reprod Toxicol 101(1):1–2

    Article  CAS  Google Scholar 

  • Matthiessen P, Wheeler JR, Weltje L (2018) A review of the evidence for endocrine disrupting effects of current-use chemicals on wildlife populations. Crit Rev Toxicol 48(3):195–216

    Article  CAS  Google Scholar 

  • Ma JY, Li YW, Mei N, Tian HD, Wang X (2018) Study on interaction between brominated flame retardants and transthyretin by native electrospray ionization mass spectrometry (in Chinese). J Instrum Anal 37(5): 525–531

    Google Scholar 

  • Murk AJ, Rijntjes E, Blaauboer BJ, Clewell R, Crofton KM, Dingemans MM, Furlow JD, Kavlock R, Köhrle J, Opitz R, Traas T, Visser TJ, Xia M, Gutleb AC (2013) Mechanism-based testing strategy using in vitro approaches for identification of thyroid hormone disrupting chemicals. Toxicol in Vitro 27(4):1320–1346

    Article  CAS  Google Scholar 

  • Organization for Economic Co-Operation and Development (2018) Revised Guidance Document 150 on Standardised Test Guidelines for Evaluating Chemicals for Endocrine Disruption, Organization for Economic Co-Operation and Development (OECD) Series on Testing and Assessment, OECD Publishing, Paris, pp 20–21, https://doi.org/10.1787/9789264304741-en

  • Papalou O, Kandaraki EA, Papadakis G, Diamanti-Kandarakis E (2019) Endocrine disrupting chemicals: an occult mediator of metabolic disease. Front Endocrinol (lausanne) 10:112

    Article  Google Scholar 

  • Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, Blondel M, Prettenhofer P, Weiss R, Dubourg V, Vanderplas J, Passos A, Cournapeau D, Brucher M, Perrot M, Duchesnay É (2011) Scikit-learn: machine learning in Python. J Mach Learn Res 12:2825–2830

    Google Scholar 

  • Roncaglioni A, Piclin N, Pintore M, Benfenati E (2008) Binary classification models for endocrine disrupter effects mediated through the estrogen receptor. SAR QSAR Environ Res 19(7–8):697–733

    Article  CAS  Google Scholar 

  • Rotroff DM, Dix DJ, Houck KA, Knudsen TB, Martin MT, McLaurin KW, Reif DM, Crofton KM, Singh AV, Xia M, Huang R, Judson RS (2013) Using in vitro high throughput screening assays to identify potential endocrine-disrupting chemicals. Environ Health Perspect 121(1):7–14

    Article  Google Scholar 

  • Sakkiah S, Guo WJ, Pan BH, Kusko R, Tong WD, Hong HX (2018) Computational prediction models for assessing endocrine disrupting potential of chemicals. J Environ Sci Health C Environ Carcinog Ecotoxicol Rev 36(4):192–218

    Article  CAS  Google Scholar 

  • Sakkiah S, Selvaraj C, Guo W, Liu J, Ge W, Patterson TA, Hong H (2021) Elucidation of agonist and antagonist dynamic binding patterns in ER-α by integration of molecular docking, molecular dynamics simulations and quantum mechanical calculations. Int J Mol Sci 22(17):9371

    Article  CAS  Google Scholar 

  • Šauer P, Švecová H, Grabicová K, Gönül Aydın F, Mackuľak T, Kodeš V, Blytt LD, Henninge LB, Grabic R, Kocour Kroupová H (2021) Bisphenols emerging in Norwegian and Czech aquatic environments show transthyretin binding potency and other less-studied endocrine-disrupting activities. Sci Total Environ 751:141801

    Article  Google Scholar 

  • Shenoy K, Crowley PH (2011) Endocrine disruption of male mating signals: ecological and evolutionary implications. Funct Ecol 25:433–448

    Article  Google Scholar 

  • Tan H, Chen Q, Hong H, Benfenati E, Gini GC, Zhang X, Yu H, Shi W (2021) Structures of endocrine-disrupting chemicals correlate with the activation of 12 classic nuclear receptors. Environ Sci Technol 55(24):16552–16562

    Article  CAS  Google Scholar 

  • Tang W, Chen J, Hong H (2020) Development of classification models for predicting inhibition of mitochondrial fusion and fission using machine learning methods. Chemosphere 273:128567

    Article  Google Scholar 

  • Tang W, Chen J, Wang Z, Xie H, Hong H (2018) Deep learning for predicting toxicity of chemicals: a mini review. J Environ Sci Health C Environ Carcinog Ecotoxicol Rev 36(4):252–271

    Article  CAS  Google Scholar 

  • United Nations Environment Programme/World Health Organization (2013) State of the science of endocrine disrupting chemicals. United Nations Environment Programme/World Health Organization (UNEP/WHO), Geneva, pp 23–188

    Google Scholar 

  • Vedani A, Smiesko M, Spreafico M, Peristera O, Dobler M (2009) VirtualToxLab—in silico prediction of the toxic (endocrine-disrupting) potential of drugs, chemicals and natural products. Two years and 2000 compounds of experience: a progress report. ALTEX 26(3):167–176

    Google Scholar 

  • Wang J, Hallinger DR, Murr AS, Buckalew AR, Simmons SO, Laws SC, Stoker TE (2018) High-Throughput screening and quantitative chemical ranking for sodium-iodide symporter Inhibitors in ToxCast phase I chemical library. Environ Sci Technol 52(9):5417–5426

    Article  CAS  Google Scholar 

  • Wang J, Hallinger DR, Murr AS, Buckalew AR, Lougee RR, Richard AM, Laws SC, Stoker TE (2019a) High-throughput screening and chemotype-enrichment analysis of ToxCast phase II chemicals evaluated for human sodium-iodide symporter (NIS) inhibition. Environ Int 126:377–386

    Article  CAS  Google Scholar 

  • Wang J, Richard AM, Murr AS, Buckalew AR, Lougee RR, Shobair M, Hallinger DR, Laws SC, Stoker TE (2021a) Expanded high-throughput screening and chemotype-enrichment analysis of the phase II: e1k ToxCast library for human sodium-iodide symporter (NIS) inhibition. Arch Toxicol 95(5):1723–1737

    Article  CAS  Google Scholar 

  • Wang L, Zhao L, Liu X, Fu J, Zhang A (2021b) SepPCNET: deeping learning on a 3D surface electrostatic potential point cloud for enhanced toxicity classification and its application to suspected environmental estrogens. Environ Sci Technol 55(14):9958–9967

    Article  CAS  Google Scholar 

  • Wang MWH, Goodman JM, Allen THE (2021c) Machine learning in predictive toxicology: recent applications and future directions for classification models. Chem Res Toxicol 34(2):217–239

    Article  CAS  Google Scholar 

  • Wang YN, Liu HH, Yang XH (2019b) Development of binary classification models for predicting estrogenic activity of organic compounds on zebrafish. Asian J Ecotoxicol 14(4):163–169 (in Chinese)

    Google Scholar 

  • Wang Z, Chen J, Hong H (2021d) Developing QSAR models with defined applicability domains on PPARγ binding affinity using large data sets and machine learning algorithms. Environ Sci Technol 55(10):6857–6866

    Article  CAS  Google Scholar 

  • Wang Z, Walker GW, Muir DCG, Nagatani-Yoshida K (2020) Toward a global understanding of chemical pollution: a first comprehensive analysis of national and regional chemical inventories. Environ Sci Technol 54(5):2575–2584

    Article  CAS  Google Scholar 

  • Xi Y, Yang X, Zhang H, Liu H, Watson P, Yang F (2020) Binding interactions of halo-benzoic acids, halo-benzenesulfonic acids and halo-phenylboronic acids with human transthyretin. Chemosphere 242:125135

    Article  CAS  Google Scholar 

  • Yap CW (2011) PaDEL-Descriptor: an open source software to calculate molecular descriptors and fingerprints. J Comput Chem 32(7):1466–1474

    Article  CAS  Google Scholar 

  • Yang H, Lou C, Sun L, Li J, Cai Y, Wang Z, Li W, Liu G, Tang Y (2019) admetSAR 2.0: web-service for prediction and optimization of chemical ADMET properties. Bioinformatics 35(6):1067–1069

    Google Scholar 

  • Yang XH, Ou W, Zhao SS, Xi Y, Wang LJ, Liu HH (2021) Rapid screening of human transthyretin disruptors through a tiered in silico approach. ACS Sustain Chem Eng 9(16):5661–5672

    Article  CAS  Google Scholar 

  • Yang X, Xie H, Chen J, Li X (2013) Anionic phenolic compounds bind stronger with transthyretin than their neutral forms: nonnegligible mechanisms in virtual screening of endocrine disrupting chemicals. Chem Res Toxicol 26(9):1340–1347

    Article  CAS  Google Scholar 

  • Yin C, Yang XH, Wei MB, Liu HH (2017) Predictive models for identifying the binding activity of structurally diverse chemicals to human pregnane X receptor. Environ Sci Pollut Res 24(24):20063–20071

    Article  CAS  Google Scholar 

  • Zhong S, Zhang K, Bagheri M, Burken JG, Gu A, Li B, Ma X, Marrone BL, Ren ZJ, Schrier J, Shi W, Tan H, Wang T, Wang X, Wong BM, Xiao X, Yu X, Zhu JJ, Zhang H (2021) Machine learning: new ideas and tools in environmental science and engineering. Environ Sci Technol 55(19):12741–12754

    CAS  Google Scholar 

  • Zhu H, Zhang J, Kim MT, Boison A, Sedykh A, Moran K (2014) Big data in chemical toxicity research: the use of high-throughput screening assays to identify potential toxicants. Chem Res Toxicol 27(10):1643–1651

    Article  CAS  Google Scholar 

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Acknowledgements

The study was supported by the National Natural Science Foundation of China (No. 22176097), China Postdoctoral Science Foundation (2020T130301, 2020M671502); Jiangsu Planned Projects for Postdoctoral Research Funds (2020Z288).

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Authors and Affiliations

  1. Jiangsu Key Laboratory of Chemical Pollution Control and Resources Reuse, School of Environmental and Biological Engineering, Nanjing University of Science and Technology, Nanjing, 210094, China

    Xianhai Yang & Huihui Liu

  2. Immuneering Corporation, Cambridge, MA, 02142, USA

    Rebecca Kusko

  3. National Center for Toxicological Research US Food and Drug Administration, Jefferson, AR, 72079, USA

    Huixiao Hong

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  1. Xianhai Yang
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  2. Huihui Liu
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Correspondence to Xianhai Yang .

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  1. Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR, USA

    Huixiao Hong

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Yang, X., Liu, H., Kusko, R., Hong, H. (2023). ED Profiler: Machine Learning Tool for Screening Potential Endocrine-Disrupting Chemicals. In: Hong, H. (eds) Machine Learning and Deep Learning in Computational Toxicology. Computational Methods in Engineering & the Sciences. Springer, Cham. https://doi.org/10.1007/978-3-031-20730-3_10

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