Generative AI-Enhanced Data Engineering Pipelines for Predictive Biomarker Discovery in Alzheimer’s and Kidney Disease

Abstract

Generative AI-Enhanced Data Engineering Pipelines for Predictive Biomarker Discovery in Alzheimer’s Disease and Kidney Disease: an objective, evidence-based, formal study of methodologies, architectures, and implications, with clear, parsimonious argumentation and rigorous evaluation. Concise, objective synthesis of the study’s aims, hypotheses, scope, and contributions; specification of research questions; expected impact on biomarker discovery. The clinical significance of Alzheimer’s disease risk-modifying biomarkers is widely accepted. Nevertheless, despite pervasive data science activity in the search for predictive biomarkers, DNA-based predictors remain elusive, proteomic-based predictors are too often unreplicated, and AI-based predictors are often unvalidated and poorly understood. The soaring number of data repositories holds great potential for the discovery of predictive disease biomarkers; however, issues with data quality, integration, reproducibility, and lack of adequate engineering pipelines hinder this promise. Existing full data engineering pipelines are rarely employed. Generative AI is a novel, emerging area of research and application with potential to transform traditional information-technology and data-engineering infrastructure, with broad implications for data engineering for Alzheimer’s disease, kidney disease, and the search for other predictive disease biomarkers.

Ähnliche Arbeiten