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Area of Science:

  • Genomics and Precision Medicine
  • Computational Biology
  • Artificial Intelligence in Healthcare

Background:

  • Precision medicine aims to tailor disease diagnosis and treatment using genetic, environmental, and lifestyle factors.
  • Genomic data is central to precision medicine but presents significant analytical challenges.
  • Integrating diverse genomic data across populations and diseases requires advanced computational methods.

Purpose of the Study:

  • To review and compare artificial intelligence (AI) and machine learning (ML) approaches in genomics and precision medicine.
  • To analyze scientific objectives, methodologies, datasets, data sources, ethics, and research gaps.
  • To identify widely adapted AI/ML algorithms for predictive diagnostics in genomics.

Main Methods:

  • Systematic literature review of high-quality studies published within the last 5 years, indexed in PubMed Central.
  • Focused on AI/ML applications in statistical and predictive analyses of whole genome/exome sequencing (gene variants) and RNA-seq/microarrays (gene expression).
  • Comparative analysis of identified AI/ML approaches across various genomics studies.

Main Results:

  • Identified 32 distinct AI/ML approaches applied in genomics research.
  • Reported commonly used AI/ML algorithms for predictive diagnostics across multiple diseases.
  • Highlighted the growing role of AI/ML in analyzing complex genomic data for precision medicine applications.

Conclusions:

  • AI and ML methods are essential for overcoming the challenges of integrating genomic data into precision medicine.
  • The review provides a comprehensive overview of current AI/ML applications, methodologies, and their impact on predictive diagnostics.
  • Further research is needed to address identified gaps and optimize AI/ML utilization across diverse populations and diseases.