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7.1.1 High-throughput screening and multi-omics integration 7.1.1.1 Single-cell resolution technologies Single-cell transcriptomics (scRNA-seq) and chromatin accessibility sequencing (ATAC-seq) enable unprecedented cell-type-specific analysis of EED-induced epigenetic and transcriptional alterations ( Single-cell methylation profiling using nanopore sequencing or single-cell bisulfite sequencing represents a transformative advance in detecting sperm-specific epigenetic alterations associated with transgenerational inheritance ( 7.1.1.2 Artificial intelligence integration AI-driven predictive modeling approaches integrate multi-omics datasets encompassing genomics, proteomics, and metabolomics to assess individual susceptibility to EED exposure ( 7.1.2 Personalized biomonitoring and risk stratification 7.1.2.1 Wearable biosensor technology Advancement of portable analytical devices for simplified detection of urinary EED metabolites, including rapid screening methods for BPA and phthalates in clinical and occupational settings ( 7.1.2.2 Machine learning risk prediction Machine learning algorithms trained on comprehensive epidemiological datasets may accurately predict reproductive health outcomes based on individual cumulative EED exposure profiles ( 7.2 Mechanistic studies on low-dose and mixture effects Understanding the biological effects of low-dose chronic EED exposure and real-world chemical mixtures remains crucial for accurate risk assessment and regulatory decision-making