Associations between persistent organic pollutants and endometriosis: A multipollutant assessment using machine learning algorithms.

[1]  J. Sampson Metastatic or Embolic Endometriosis, due to the Menstrual Dissemination of Endometrial Tissue into the Venous Circulation. , 1927, The American journal of pathology.

[2]  Martin A. Cohen,et al.  Observations less than the analytical limit of detection , 1989 .

[3]  A M Walker,et al.  Epidemiologic interpretation of artificial neural networks. , 1998, American journal of epidemiology.

[4]  J. Friedman Special Invited Paper-Additive logistic regression: A statistical view of boosting , 2000 .

[5]  T. Tape,et al.  Interpretation of Diagnostic Tests , 2001, Annals of Internal Medicine.

[6]  P. Vercellini,et al.  Serum dioxin concentrations and endometriosis: a cohort study in Seveso, Italy. , 2002, Environmental health perspectives.

[7]  Jason Weston,et al.  Gene Selection for Cancer Classification using Support Vector Machines , 2002, Machine Learning.

[8]  B. Whitcomb,et al.  Environmental PCB exposure and risk of endometriosis. , 2005, Human reproduction.

[9]  H. Zou,et al.  Regularization and variable selection via the elastic net , 2005 .

[10]  Robert H. Riffenburgh Chapter 15 – Tests on Categorical Data , 2006 .

[11]  Philippe Besse,et al.  Statistical Applications in Genetics and Molecular Biology A Sparse PLS for Variable Selection when Integrating Omics Data , 2011 .

[12]  Max Kuhn,et al.  Building Predictive Models in R Using the caret Package , 2008 .

[13]  Robert Tibshirani,et al.  The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd Edition , 2001, Springer Series in Statistics.

[14]  B. Le Bizec,et al.  Exposure assessment of French women and their newborn to brominated flame retardants: determination of tri- to deca- polybromodiphenylethers (PBDE) in maternal adipose tissue, serum, breast milk and cord serum. , 2009, Environmental pollution.

[15]  John J. Heine,et al.  Statistical learning techniques applied to epidemiology: a simulated case-control comparison study with logistic regression , 2011, BMC Bioinformatics.

[16]  K. L. Bruner-Tran,et al.  Dioxin and Endometrial Progesterone Resistance , 2010, Seminars in reproductive medicine.

[17]  L. Giudice Clinical practice. Endometriosis. , 2010, The New England journal of medicine.

[18]  Jason Weston,et al.  A user's guide to support vector machines. , 2010, Methods in molecular biology.

[19]  Philippe Besse,et al.  Sparse PLS discriminant analysis: biologically relevant feature selection and graphical displays for multiclass problems , 2011, BMC Bioinformatics.

[20]  K. L. Bruner-Tran,et al.  Dioxin-like PCBs and Endometriosis , 2010, Systems biology in reproductive medicine.

[21]  Isabella Annesi-Maesano,et al.  Estimating the health effects of exposure to multi-pollutant mixture. , 2012, Annals of epidemiology.

[22]  A. Roy,et al.  Assessing Chemical Mixtures and Human Health: Use of Bayesian Belief Net Analysis. , 2012, Journal of environmental protection.

[23]  Paul S Albert,et al.  Latent class models for joint analysis of disease prevalence and high-dimensional semicontinuous biomarker data. , 2012, Biostatistics.

[24]  Bin Yu,et al.  Estimation Stability With Cross-Validation (ESCV) , 2013, 1303.3128.

[25]  M. Thompson,et al.  Organochlorine Pesticides and Risk of Endometriosis: Findings from a Population-Based Case–Control Study , 2013, Environmental health perspectives.

[26]  Bhramar Mukherjee,et al.  Statistical strategies for constructing health risk models with multiple pollutants and their interactions: possible choices and comparisons , 2013, Environmental Health.

[27]  Aldert H Piersma,et al.  Phthalates, perfluoroalkyl acids, metals and organochlorines and reproductive function: a multipollutant assessment in Greenlandic, Polish and Ukrainian men , 2014, Occupational and Environmental Medicine.

[28]  B. Le Bizec,et al.  Ultra-trace quantification method for chlordecone in human fluids and tissues. , 2015, Journal of chromatography. A.

[29]  Mostafa El Qannari,et al.  ClustVarLV: An R Package for the Clustering of Variables Around Latent Variables , 2015, R J..

[30]  S. Ploteau,et al.  Distribution of persistent organic pollutants in serum, omental, and parietal adipose tissue of French women with deep infiltrating endometriosis and circulating versus stored ratio as new marker of exposure. , 2016, Environment international.

[31]  Chris Gennings,et al.  Statistical Approaches for Assessing Health Effects of Environmental Chemical Mixtures in Epidemiology: Lessons from an Innovative Workshop , 2016, Environmental health perspectives.

[32]  Paolo Vineis,et al.  A Systematic Comparison of Linear Regression–Based Statistical Methods to Assess Exposome-Health Associations , 2016, Environmental health perspectives.

[33]  Aldert H Piersma,et al.  Prenatal Phthalate, Perfluoroalkyl Acid, and Organochlorine Exposures and Term Birth Weight in Three Birth Cohorts: Multi-Pollutant Models Based on Elastic Net Regression , 2015, Environmental health perspectives.

[34]  Paolo Vineis,et al.  A systematic comparison of statistical methods to detect interactions in exposome-health associations , 2017, Environmental Health.

[35]  P. Martín-Olmedo,et al.  Human adipose tissue levels of persistent organic pollutants and metabolic syndrome components: Combining a cross-sectional with a 10-year longitudinal study using a multi-pollutant approach. , 2017, Environment international.

[36]  S. Ploteau,et al.  Associations between internal exposure levels of persistent organic pollutants in adipose tissue and deep infiltrating endometriosis with or without concurrent ovarian endometrioma. , 2017, Environment international.

[37]  Roel Vermeulen,et al.  Performance of variable selection methods for assessing the health effects of correlated exposures in case–control studies , 2017, Occupational and Environmental Medicine.

[38]  Gaurav Pandey,et al.  Using machine learning to identify air pollution exposure profiles associated with early cognitive skills among U.S. children. , 2017, Environmental pollution.

[39]  Molly A. Hall,et al.  Informatics and Data Analytics to Support Exposome-Based Discovery for Public Health. , 2017, Annual review of public health.

[40]  Enrique F Schisterman,et al.  Collinearity and Causal Diagrams: A Lesson on the Importance of Model Specification , 2017, Epidemiology.

[41]  Regina Hampel,et al.  Statistical Approaches to Address Multi-Pollutant Mixtures and Multiple Exposures: the State of the Science , 2017, Current Environmental Health Reports.

[42]  Osmar R Zaiane,et al.  A systematic review of data mining and machine learning for air pollution epidemiology , 2017, BMC Public Health.

[43]  Syam S Andra,et al.  Trends in the application of high-resolution mass spectrometry for human biomonitoring: An analytical primer to studying the environmental chemical space of the human exposome. , 2017, Environment international.

[44]  Jun Deng,et al.  Predicting non-melanoma skin cancer via a multi-parameterized artificial neural network , 2018, Scientific Reports.

[45]  Marc G. Weisskopf,et al.  Bias Amplification in Epidemiologic Analysis of Exposure to Mixtures , 2018, Environmental health perspectives.

[46]  Caspar G. Chorus,et al.  Is your dataset big enough? Sample size requirements when using artificial neural networks for discrete choice analysis , 2018, Journal of Choice Modelling.

[47]  Paolo Vineis,et al.  A multivariate approach to investigate the combined biological effects of multiple exposures , 2018, Journal of Epidemiology & Community Health.

[48]  Jessie P Buckley,et al.  Environmental Exposure Mixtures: Questions and Methods to Address Them , 2018, Current Epidemiology Reports.

[49]  Jamile Silveira Tomiazzi,et al.  Performance of machine-learning algorithms to pattern recognition and classification of hearing impairment in Brazilian farmers exposed to pesticide and/or cigarette smoke , 2019, Environmental Science and Pollution Research.

[50]  Yu Tian,et al.  Machine Learning Models for the Hearing Impairment Prediction in Workers Exposed to Complex Industrial Noise: A Pilot Study , 2019, Ear and hearing.

[51]  K. Matta,et al.  Associations between exposure to organochlorine chemicals and endometriosis in experimental studies: A systematic review protocol. , 2019, Environment international.

[52]  K. Matta,et al.  Human epidemiological evidence about the associations between exposure to organochlorine chemicals and endometriosis: Systematic review and meta-analysis. , 2019, Environment international.

[53]  E. Papadopoulou,et al.  Diet as a Source of Exposure to Environmental Contaminants for Pregnant Women and Children from Six European Countries , 2019, Environmental health perspectives.

[54]  Andrea Bellavia,et al.  Approaches for incorporating environmental mixtures as mediators in mediation analysis. , 2019, Environment international.

[55]  M. Kogevinas,et al.  The mediating effect of immune markers on the association between ambient air pollution and adult-onset asthma , 2019, Scientific Reports.

[56]  Matthias Ketzel,et al.  A comparison of linear regression, regularization, and machine learning algorithms to develop Europe-wide spatial models of fine particles and nitrogen dioxide. , 2019, Environment international.

[57]  B. Le Bizec,et al.  The challenging use and interpretation of blood biomarkers of exposure related to lipophilic endocrine disrupting chemicals in environmental health studies , 2020, Molecular and Cellular Endocrinology.