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Medical research accepts personalized medicine largely by combining genomic analysis with computational biology and bioinformatics methods to investigate biological systems and disease principles simultaneously. The fusion of NGS sequencing protocols and proteomic analysis accompanied by mass spectrometry enables breakthrough treatment solutions mostly in cases of rare diseases alongside cancer. The research explores contemporary data unification patterns for personalized medicine which integrate multi-omics data frameworks that unite genomic and transcriptomic and proteomic and metabolomic systems alongside artificial intelligence systems for precision medicine applications. This paper examined research articles located in Scopus database from 2020 to 2025 through scientometric methods. Cite Space and VOS viewer generated maps that analyzed AI-enhance multi-omics integration in genomic research through examining both co-occurrence relationships and collaboration networks and topic-based clustering connections. This study evaluated 781 research materials from reviews and journal publications and conference papers. The study shows machine learning and AI techniques boost their capabilities to unite multi-omics information into individual treatment approaches. The field establishes precise oncology research through analysis of pharmacogenomics and examines data privacy matters with AI implementation demands.

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Exploring Emerging Trends and Key Challenges in Bioinformatics, Genomics, and Computational Biology Data Integration Approaches for Advancing Personalized Medicine and Genomic Research

  • 会议论文

  • 发表日期：

    2026年

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  • Nagendar Yamsani,
  • G. Shyamala,
  • A. Rajeshwar Rao,
  • Sai Anjani Manda

摘要

Medical research accepts personalized medicine largely by combining genomic analysis with computational biology and bioinformatics methods to investigate biological systems and disease principles simultaneously. The fusion of NGS sequencing protocols and proteomic analysis accompanied by mass spectrometry enables breakthrough treatment solutions mostly in cases of rare diseases alongside cancer. The research explores contemporary data unification patterns for personalized medicine which integrate multi-omics data frameworks that unite genomic and transcriptomic and proteomic and metabolomic systems alongside artificial intelligence systems for precision medicine applications. This paper examined research articles located in Scopus database from 2020 to 2025 through scientometric methods. Cite Space and VOS viewer generated maps that analyzed AI-enhance multi-omics integration in genomic research through examining both co-occurrence relationships and collaboration networks and topic-based clustering connections. This study evaluated 781 research materials from reviews and journal publications and conference papers. The study shows machine learning and AI techniques boost their capabilities to unite multi-omics information into individual treatment approaches. The field establishes precise oncology research through analysis of pharmacogenomics and examines data privacy matters with AI implementation demands.