Intelligent Data Engineering and Generative AI for Biomarker Discovery in Neurodegenerative and Kidney Diseases
DOI:
https://doi.org/10.63282/3117-5481/AIJCST-V6I4P109Keywords:
Generative Artificial Intelligence, Data Engineering Pipelines, Predictive Biomarker Discovery, Alzheimer’s Disease Analytics, Kidney Disease Analytics, MultiOmics Data Integration, Clinical Data Harmonization, Reproducible Research Pipelines, Scalable Biomedical Architectures, Metadata Standardization, Data Quality Control, Synthetic Data Generation, Feature Representation Learning, Explainable Biomarker Models, Translational Bioinformatics, Cloud Native Data Infrastructure, Model Validation Frameworks, Evidence Based Discovery, Precision Medicine Enablement, Lifecycle Oriented Data EngineeringAbstract
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. Generative AI is increasingly being used in the biomedical domain. Nevertheless, generative-AI-enhanced data-engineering pipelines that support the entire data-flow lifecycle of predictive biomarker discovery remain to be published. Questions include how generative AI can enhance pipelines, what data engineering contributions will be important to pipeline success, and how qualitative pipeline success will be achieved. The pipeline built-in for-preparation, data-acquisition, -curation, -integration, -preprocessing, -quality-control, and -metadata-standard definition is described, with special attention to balancing reproducibility, flexibility, and scalability. Development subcomponents include a state-of-the-art age-grouped biomarker list for healthy-adult-status monitoring and DNA-typical and predictive-biomarker-quality-validation-or-approach-type-typical models and approaches for data-harmonization quality control.
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