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This research aims to present advanced predictive models that seamlessly integrate multi-omics data (genomics, transcriptomics, proteomics, etc.) with clinical notes. The goal is to significantly improve the accuracy of disease diagnosis, enhance prognosis predictions, and enable personalized treatment strategies. The research approach includes a mixed-methods design, combining quantitative and qualitative methods. It involves data pre-processing techniques to standardize multi-omics data, advanced natural language processing (NLP) for extracting structured insights from clinical notes, and the development of interpretable predictive models. The research also focuses on clinical translation through the creation of user-friendly interfaces and adheres to strict ethical and regulatory guidelines to ensure responsible data usage. The research contributes both theoretically and practically to the fields of multi-omics integration, NLP in healthcare, and predictive modeling. Theoretical advancements encompass a deeper understanding of data integration, NLP techniques, and model transparency in healthcare. Practically, this research offers prospects for more accurate disease diagnosis, enhanced prognostication, and personalized treatment strategies, thereby improving clinical workflows and ensuring ethical data practices.

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Integrating Multi-omics and Clinical Narratives for Predictive Modeling: Genomics, Transcriptomics, Proteomics, and Medical Texts in Disease Analysis

  • 会议论文

  • 发表日期：

    2025年

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  • Sohaib Irfan,
  • Rao Nouman Ali,
  • Rao Faizan Ali,
  • Bilal Hassan

摘要

This research aims to present advanced predictive models that seamlessly integrate multi-omics data (genomics, transcriptomics, proteomics, etc.) with clinical notes. The goal is to significantly improve the accuracy of disease diagnosis, enhance prognosis predictions, and enable personalized treatment strategies. The research approach includes a mixed-methods design, combining quantitative and qualitative methods. It involves data pre-processing techniques to standardize multi-omics data, advanced natural language processing (NLP) for extracting structured insights from clinical notes, and the development of interpretable predictive models. The research also focuses on clinical translation through the creation of user-friendly interfaces and adheres to strict ethical and regulatory guidelines to ensure responsible data usage. The research contributes both theoretically and practically to the fields of multi-omics integration, NLP in healthcare, and predictive modeling. Theoretical advancements encompass a deeper understanding of data integration, NLP techniques, and model transparency in healthcare. Practically, this research offers prospects for more accurate disease diagnosis, enhanced prognostication, and personalized treatment strategies, thereby improving clinical workflows and ensuring ethical data practices.