Research graph
References from Stability-oriented chemical–biological integration prioritizes antimicrobials linked to resistome variation in landfill leachates across China. Local targets link to admitted publications; unresolved targets remain external evidence.
Recursive partitioning for heterogeneous causal effects
10.1073/pnas.1510489113 · 2016 · External reference
Concentrations of antibiotics predicted to select for resistant bacteria: proposed limits for environmental regulation
10.1016/j.envint.2015.10.015 · 2016 · External reference
Using machine learning tools to model complex toxic interactions with limited sampling regimes
10.1021/es3033549 · 2013 · External reference
Hydrolysis of sulphonamides in aqueous solutions
10.1016/j.jhazmat.2012.04.044 · 2012 · External reference
Recent developments in causal inference and machine learning
10.1146/annurev-soc-030420-015345 · 2023 · External reference
A machine-learning approach clarifies interactions between contaminants of emerging concern
10.1016/j.oneear.2022.10.006 · 2022 · External reference
Fate characteristics, exposure risk, and control strategy of typical antibiotics in Chinese sewerage system: a review
10.1016/j.envint.2022.107396 · 2022 · External reference
Big data integration for environmental risk assessment of emerging contaminants
10.1038/s41893-025-01718-2 · 2026 · External reference
Machine learning applications for chemical fingerprinting and environmental source tracking using non-target chemical data
10.1021/acs.est.1c06655 · 2022 · External reference
Clarifying the effect of biodiversity on productivity in natural ecosystems with longitudinal data and methods for causal inference
10.1038/s41467-023-37194-5 · 2023 · External reference
Machine learning methods for small data challenges in molecular science
10.1021/acs.chemrev.3c00189 · 2023 · External reference
Unveiling the occurrence, distribution, removal, and environmental impacts of 65 emerging contaminants in neglected fresh leachate from municipal solid waste incineration plants
10.1016/j.jhazmat.2023.132355 · 2023 · External reference
Antibiotic concentrations and antibiotic resistance in aquatic environments of the WHO Western Pacific and South-East Asia regions: a systematic review and probabilistic environmental hazard assessment
10.1016/s2542-5196(22)00254-6 · 2023 · External reference
Antibiotic resistance contamination in four Italian municipal solid waste landfills sites spanning 34 years
10.1016/j.chemosphere.2020.129182 · 2021 · External reference
Understanding the mechanisms and drivers of antimicrobial resistance
10.1016/s0140-6736(15)00473-0 · 2016 · External reference
Antibiotic resistome in landfill leachate and impact on groundwater
10.1016/j.scitotenv.2024.171991 · 2024 · External reference
Identification of key driving factors for ecological environmental quality in Hainan tropical rainforest national park using causal inference with double machine learning
2025 · External reference
Utilizing stability criteria in choosing feature selection methods yields reproducible results in microbiome data
10.1111/biom.13481 · 2021 · External reference
Stability of feature selection algorithm: a review
10.1016/j.jksuci.2019.06.012 · 2022 · External reference
Towards the integration of antibiotic resistance gene mobility into environmental surveillance and risk assessment
10.1038/s44259-025-00154-8 · 2025 · External reference
AMR, one health and the environment
10.1038/s41564-023-01351-9 · 2023 · External reference
Differential effects of wastewater treatment plant effluents on the antibiotic resistomes of diverse river habitats
10.1038/s41396-023-01506-w · 2023 · External reference
The panorama of antibiotics and the related antibiotic resistance genes (ARGs) in landfill leachate
10.1016/j.wasman.2022.03.008 · 2022 · External reference
A stable feature selection method based on majority voting and SHAP for high-dimensional metabolomics data
10.1016/j.cmpb.2025.109170 · 2026 · External reference
Chemical and biological assessments of environmental mixtures: a review of current trends, advances, and future perspectives
10.1016/j.jhazmat.2022.128658 · 2022 · External reference
The NORMAN suspect list exchange (NORMAN-SLE): facilitating European and worldwide collaboration on suspect screening in high resolution mass spectrometry
10.1186/s12302-022-00680-6 · 2022 · External reference
A critical meta-analysis of predicted no effect concentrations for antimicrobial resistance selection in the environment
10.1016/j.watres.2024.122310 · 2024 · External reference
A review of machine learning with small and limited data
10.1186/s40537-025-01346-9 · 2026 · External reference
Modulation of antibiotic effects on microbial communities by resource competition
10.1038/s41467-023-37895-x · 2023 · External reference
Interpretable machine learning for predicting the fate and transport of pentachlorophenol in groundwater
10.1016/j.envpol.2024.123449 · 2024 · External reference
Occurrence of pharmaceuticals and plasticizers in leachate from municipal landfills of different age
10.1016/j.wasman.2022.01.023 · 2022 · External reference
Modern causal inference approaches to investigate biodiversity-ecosystem functioning relationships
10.1038/s41467-023-37546-1 · 2023 · External reference
Linking antibiotic resistance gene patterns with advanced faecal pollution assessment and environmental key parameters along 2300 km of the Danube river
10.1016/j.watres.2024.121244 · 2024 · External reference
Assessing heterogeneity of treatment effect in real-world data
10.7326/m22-1510 · 2023 · External reference
Pathogens and antibiotic resistance genes during the landfill leachate treatment process: occurrence, fate, and impact on groundwater
10.1016/j.scitotenv.2023.165925 · 2023 · External reference
Quantification of the covariation of lake microbiomes and environmental variables using a machine learning-based framework
10.1111/mec.15872 · 2021 · External reference
Metallic nanoparticles induced antibiotic resistance genes attenuation of leachate culturable microbiota: the combined roles of growth inhibition, ion dissolution and oxidative stress
10.1016/j.envint.2019.05.007 · 2019 · External reference
Subsurface landfill leachate contamination affects microbial metabolic potential and gene expression in the banisveld aquifer
2018 · External reference
Estimation and inference of heterogeneous treatment effects using random forests
10.1080/01621459.2017.1319839 · 2018 · External reference
Emerging contaminants: a one health perspective
2024 · External reference
Addressing the data scarcity problem in ecotoxicology via small data machine learning methods
10.1021/acs.est.5c00510 · 2025 · External reference
Antibiotic resistance genes in landfill leachates from seven municipal solid waste landfills: seasonal variations, hosts, and risk assessment
10.1016/j.scitotenv.2022.158677 · 2022 · External reference
Relationships between antibiotics and antibiotic resistance gene levels in municipal solid waste leachates in Shanghai, China
10.1021/es506081z · 2015 · External reference
Identification of indicator PPCPs in landfill leachates and livestock wastewaters using multi-residue analysis of 70 PPCPs: analytical method development and application in Yangtze River Delta, China
10.1016/j.scitotenv.2020.141653 · 2021 · External reference
Geographic patterns and determinants of antibiotic resistomes in coastal sediments across complex ecological gradients
10.3389/fmicb.2022.922580 · 2022 · External reference
Impact of low-dose free chlorine on the conjugative transfer of antibiotic resistance genes in wastewater effluents: identifying key environmental factors for predictive modeling
10.1016/j.jhazmat.2024.136824 · 2025 · External reference
Historical trajectories of antibiotics resistance genes assessed through sedimentary DNA analysis of a subtropical eutrophic lake
10.1016/j.envint.2024.108654 · 2024 · External reference
Dealing with systemic environmental risks
10.1038/s41893-025-01525-9 · 2025 · External reference
Co-occurrence of mobile genetic elements and antibiotic resistance genes in municipal solid waste landfill leachates: a preliminary insight into the role of landfill age
10.1016/j.watres.2016.10.042 · 2016 · External reference
Study becomes insight: ecological learning from machine learning
10.1111/2041-210x.13686 · 2021 · External reference
A picture of pharmaceutical pollution in landfill leachates: occurrence, regional differences and influencing factors
10.1016/j.wasman.2024.05.019 · 2024 · External reference
Municipal solid waste landfills: an underestimated source of pharmaceutical and personal care products in the water environment
10.1021/acs.est.0c00565 · 2020 · External reference
Explainable and causal machine learning to investigate the spatiotemporal dynamics patterns of coastal water quality in Hong Kong
10.1016/j.watres.2025.125026 · 2026 · External reference
Antibiotic resistome in landfill leachate from different cities of China deciphered by metagenomic analysis
10.1016/j.watres.2018.01.063 · 2018 · External reference
Causal machine learning with interpretability deciphers the impact of micropollutants and socioeconomic factors on ARGs in Chinese urban drinking water
10.1016/j.envres.2026.123916 · 2026 · External reference
Machine learning in environmental research: common pitfalls and best practices
10.1021/acs.est.3c00026 · 2023 · External reference
Understanding the mechanism of microplastic-associated antibiotic resistance genes in aquatic ecosystems: insights from metagenomic analyses and machine learning
10.1016/j.watres.2024.122570 · 2025 · External reference
Antibiotic resistance contamination in four Italian municipal solid waste landfills sites spanning 34 years
10.1016/j.chemosphere.2020.129182 · ExternalCitation · doi-reference
A stable feature selection method based on majority voting and SHAP for high-dimensional metabolomics data
10.1016/j.cmpb.2025.109170 · ExternalCitation · doi-reference
Concentrations of antibiotics predicted to select for resistant bacteria: proposed limits for environmental regulation
10.1016/j.envint.2015.10.015 · ExternalCitation · doi-reference
Metallic nanoparticles induced antibiotic resistance genes attenuation of leachate culturable microbiota: the combined roles of growth inhibition, ion dissolution and oxidative stress
10.1016/j.envint.2019.05.007 · ExternalCitation · doi-reference
Fate characteristics, exposure risk, and control strategy of typical antibiotics in Chinese sewerage system: a review
10.1016/j.envint.2022.107396 · ExternalCitation · doi-reference
Historical trajectories of antibiotics resistance genes assessed through sedimentary DNA analysis of a subtropical eutrophic lake
10.1016/j.envint.2024.108654 · ExternalCitation · doi-reference
Interpretable machine learning for predicting the fate and transport of pentachlorophenol in groundwater
10.1016/j.envpol.2024.123449 · ExternalCitation · doi-reference
Causal machine learning with interpretability deciphers the impact of micropollutants and socioeconomic factors on ARGs in Chinese urban drinking water
10.1016/j.envres.2026.123916 · ExternalCitation · doi-reference
Hydrolysis of sulphonamides in aqueous solutions
10.1016/j.jhazmat.2012.04.044 · ExternalCitation · doi-reference
Chemical and biological assessments of environmental mixtures: a review of current trends, advances, and future perspectives
10.1016/j.jhazmat.2022.128658 · ExternalCitation · doi-reference
Unveiling the occurrence, distribution, removal, and environmental impacts of 65 emerging contaminants in neglected fresh leachate from municipal solid waste incineration plants
10.1016/j.jhazmat.2023.132355 · ExternalCitation · doi-reference
Impact of low-dose free chlorine on the conjugative transfer of antibiotic resistance genes in wastewater effluents: identifying key environmental factors for predictive modeling
10.1016/j.jhazmat.2024.136824 · ExternalCitation · doi-reference
Stability of feature selection algorithm: a review
10.1016/j.jksuci.2019.06.012 · ExternalCitation · doi-reference
A machine-learning approach clarifies interactions between contaminants of emerging concern
10.1016/j.oneear.2022.10.006 · ExternalCitation · doi-reference
Identification of indicator PPCPs in landfill leachates and livestock wastewaters using multi-residue analysis of 70 PPCPs: analytical method development and application in Yangtze River Delta, China
10.1016/j.scitotenv.2020.141653 · ExternalCitation · doi-reference
Antibiotic resistance genes in landfill leachates from seven municipal solid waste landfills: seasonal variations, hosts, and risk assessment
10.1016/j.scitotenv.2022.158677 · ExternalCitation · doi-reference
Pathogens and antibiotic resistance genes during the landfill leachate treatment process: occurrence, fate, and impact on groundwater
10.1016/j.scitotenv.2023.165925 · ExternalCitation · doi-reference
Antibiotic resistome in landfill leachate and impact on groundwater
10.1016/j.scitotenv.2024.171991 · ExternalCitation · doi-reference
Occurrence of pharmaceuticals and plasticizers in leachate from municipal landfills of different age
10.1016/j.wasman.2022.01.023 · ExternalCitation · doi-reference
The panorama of antibiotics and the related antibiotic resistance genes (ARGs) in landfill leachate
10.1016/j.wasman.2022.03.008 · ExternalCitation · doi-reference
A picture of pharmaceutical pollution in landfill leachates: occurrence, regional differences and influencing factors
10.1016/j.wasman.2024.05.019 · ExternalCitation · doi-reference
Co-occurrence of mobile genetic elements and antibiotic resistance genes in municipal solid waste landfill leachates: a preliminary insight into the role of landfill age
10.1016/j.watres.2016.10.042 · ExternalCitation · doi-reference
Antibiotic resistome in landfill leachate from different cities of China deciphered by metagenomic analysis
10.1016/j.watres.2018.01.063 · ExternalCitation · doi-reference
Linking antibiotic resistance gene patterns with advanced faecal pollution assessment and environmental key parameters along 2300 km of the Danube river
10.1016/j.watres.2024.121244 · ExternalCitation · doi-reference
A critical meta-analysis of predicted no effect concentrations for antimicrobial resistance selection in the environment
10.1016/j.watres.2024.122310 · ExternalCitation · doi-reference
Understanding the mechanism of microplastic-associated antibiotic resistance genes in aquatic ecosystems: insights from metagenomic analyses and machine learning
10.1016/j.watres.2024.122570 · ExternalCitation · doi-reference
Explainable and causal machine learning to investigate the spatiotemporal dynamics patterns of coastal water quality in Hong Kong
10.1016/j.watres.2025.125026 · ExternalCitation · doi-reference
Understanding the mechanisms and drivers of antimicrobial resistance
10.1016/s0140-6736(15)00473-0 · ExternalCitation · doi-reference
Antibiotic concentrations and antibiotic resistance in aquatic environments of the WHO Western Pacific and South-East Asia regions: a systematic review and probabilistic environmental hazard assessment
10.1016/s2542-5196(22)00254-6 · ExternalCitation · doi-reference
Machine learning methods for small data challenges in molecular science
10.1021/acs.chemrev.3c00189 · ExternalCitation · doi-reference
Municipal solid waste landfills: an underestimated source of pharmaceutical and personal care products in the water environment
10.1021/acs.est.0c00565 · ExternalCitation · doi-reference
Machine learning applications for chemical fingerprinting and environmental source tracking using non-target chemical data
10.1021/acs.est.1c06655 · ExternalCitation · doi-reference
Machine learning in environmental research: common pitfalls and best practices
10.1021/acs.est.3c00026 · ExternalCitation · doi-reference
Addressing the data scarcity problem in ecotoxicology via small data machine learning methods
10.1021/acs.est.5c00510 · ExternalCitation · doi-reference
Using machine learning tools to model complex toxic interactions with limited sampling regimes
10.1021/es3033549 · ExternalCitation · doi-reference
Relationships between antibiotics and antibiotic resistance gene levels in municipal solid waste leachates in Shanghai, China
10.1021/es506081z · ExternalCitation · doi-reference
Differential effects of wastewater treatment plant effluents on the antibiotic resistomes of diverse river habitats
10.1038/s41396-023-01506-w · ExternalCitation · doi-reference
Clarifying the effect of biodiversity on productivity in natural ecosystems with longitudinal data and methods for causal inference
10.1038/s41467-023-37194-5 · ExternalCitation · doi-reference
Modern causal inference approaches to investigate biodiversity-ecosystem functioning relationships
10.1038/s41467-023-37546-1 · ExternalCitation · doi-reference
Modulation of antibiotic effects on microbial communities by resource competition
10.1038/s41467-023-37895-x · ExternalCitation · doi-reference
AMR, one health and the environment
10.1038/s41564-023-01351-9 · ExternalCitation · doi-reference
Dealing with systemic environmental risks
10.1038/s41893-025-01525-9 · ExternalCitation · doi-reference
Big data integration for environmental risk assessment of emerging contaminants
10.1038/s41893-025-01718-2 · ExternalCitation · doi-reference
Towards the integration of antibiotic resistance gene mobility into environmental surveillance and risk assessment
10.1038/s44259-025-00154-8 · ExternalCitation · doi-reference
Recursive partitioning for heterogeneous causal effects
10.1073/pnas.1510489113 · ExternalCitation · doi-reference
Estimation and inference of heterogeneous treatment effects using random forests
10.1080/01621459.2017.1319839 · ExternalCitation · doi-reference
Study becomes insight: ecological learning from machine learning
10.1111/2041-210x.13686 · ExternalCitation · doi-reference
Utilizing stability criteria in choosing feature selection methods yields reproducible results in microbiome data
10.1111/biom.13481 · ExternalCitation · doi-reference
Quantification of the covariation of lake microbiomes and environmental variables using a machine learning-based framework
10.1111/mec.15872 · ExternalCitation · doi-reference
Recent developments in causal inference and machine learning
10.1146/annurev-soc-030420-015345 · ExternalCitation · doi-reference
The NORMAN suspect list exchange (NORMAN-SLE): facilitating European and worldwide collaboration on suspect screening in high resolution mass spectrometry
10.1186/s12302-022-00680-6 · ExternalCitation · doi-reference
A review of machine learning with small and limited data
10.1186/s40537-025-01346-9 · ExternalCitation · doi-reference
Geographic patterns and determinants of antibiotic resistomes in coastal sediments across complex ecological gradients
10.3389/fmicb.2022.922580 · ExternalCitation · doi-reference
Assessing heterogeneity of treatment effect in real-world data
10.7326/m22-1510 · ExternalCitation · doi-reference
