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Research and Reviews: A Journal of Pharmaceutical Science Review Article

Pathophysiology Reimagined: Integrating Systems Biology and AI for Disease Understanding

  1. Bhagvansinh Chauhan Department of Pharmacy, School of Pharmacy, Rai University, Ahmedabad
  2. Vraj Patel Department of Pharmacy, School of Pharmacy, Rai University, Ahmedabad
  3. Aakash Solanki Department of Pharmacy, School of Pharmacy, Rai University, Ahmedabad
  4. Aman Tiwari Department of Pharmacy, School of Pharmacy, Rai University, Ahmedabad
  5. Shubham Singh Department of Pharmacy, School of Pharmacy, Rai University, Ahmedabad
  6. Sanjesh Rathi Department of Pharmacy, School of Pharmacy, Rai University, Ahmedabad

Abstract

Pathophysiology, the study of disease mechanisms at molecular, cellular, and systemic levels, has traditionally relied on reductionist approaches that often fail to capture the complex, dynamic, and interconnected nature of biological systems. Diseases such as cancer, neurodegenerative disorders, and infectious diseases arise from intricate interactions among genetic, epigenetic, metabolic, and environmental factors, necessitating integrative, data-driven methodologies for a deeper understanding. Systems biology has emerged as a powerful approach by leveraging multi-omics technologies, including genomics, transcriptomics, proteomics, metabolomics, and epigenomics, to analyze disease mechanisms holistically. However, the vast and complex nature of multi-omics data presents significant challenges in integration, interpretation, and clinical application, requiring advanced computational tools. Simultaneously, artificial intelligence (AI), particularly machine learning (ML) and deep learning (DL), has revolutionized biomedical research, offering unprecedented capabilities in data analysis, biomarker discovery, disease prediction, and personalized treatment strategies. The integration of AI with systems biology presents a transformative paradigm shift in pathophysiology research, enabling more precise disease modeling, targeted therapies, and improved clinical outcomes. This review aims to explore the synergy between AI and systems biology, address key challenges such as data heterogeneity and algorithmic bias, and highlight real-world applications in neurodegenerative diseases, oncology, and infectious diseases. Despite the potential, gaps remain in fully integrating AI and systems biology for clinical translation, requiring further advancements in computational models and ethical considerations. By bridging these gaps, this review will serve as a valuable resource for researchers and clinicians, guiding the future of AI-driven pathophysiology research and personalized medicine.

Keywords

References (17)

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