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== <span style="color: #FFFFFF;">Creating</span> == Designing a genomics AI pipeline for disease risk prediction: (1) Data: obtain UK Biobank or similar population cohort GWAS summary statistics. (2) PRS construction: use PRS-CS or LDpred2 for Bayesian shrinkage estimation across all common variants. (3) Validation: validate PRS in independent cohort with different ancestry background. (4) Clinical integration: combine PRS with clinical risk factors (age, BMI, family history) using a joint logistic regression model. (5) Calibration: recalibrate model for target population; generate risk percentile scores. (6) Ethical review: ensure compliance with genomic data privacy regulations (HIPAA, GDPR); obtain IRB approval for clinical use. [[Category:Artificial Intelligence]] [[Category:Genomics]] [[Category:Bioinformatics]] </div>
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