In the modern landscape of health science, the emergence of complex diseases presents a formidable challenge, demanding cutting-edge computational methods to elucidate their complexities and devise effective therapies. Recent advancements in technologies such as next-generation sequencing and mass spectrometry have led to a proliferation of multi-omics data, revealing invaluable insights into molecular pathways, drug targets, and personalized medicine. Yet, the assimilation of these vast, diverse datasets remains hindered by issues of heterogeneity, sparsity, and high dimensionality, presenting a crucial barrier that must be overcome.

This research topic aims to propel the development of sophisticated computational techniques for multi-omics data integration. By applying the latest in artificial intelligence, machine learning, and high-performance computing, this initiative seeks to transform raw biological data into actionable clinical insights. Our focus is on creating solutions that are scalable, reproducible, and clinically relevant, thereby bridging the existing gap between laboratory research and real-world healthcare applications.

To gather further insights into smart computational methods, we welcome articles addressing, but not limited to, the following themes:

Smart algorithms for multi-omics data harmonization and fusion

AI-driven biomarker discovery in disease diagnosis and drug development

Predictive modeling for patient-specific drug responses using integrated omics

Computational approaches for disease diagnosis prediction and drug target identification

Integration of multi-omics data with clinical datasets to inform treatment pathways

Molecular interaction prediction using integrated multi-omics and intelligent computing

Reviews analyzing advancements and challenges in artificial intelligence for disease diagnosis and drug development.

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Frontiers in Pharmacology

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