In the rapidly evolving field of computational intelligence, there is a pressing demand for advanced tools and algorithms that can significantly transform our understanding of bioinformatics and biomedicine. The vast, complex, and heterogeneous nature of high-throughput single-omics, multi-omics, and multimodal biomedical data presents unique challenges that traditional analytical methods struggle to address. Recent advancements in artificial intelligence and machine learning provide robust frameworks to integrate these datasets, uncover intricate biological patterns, and develop predictive models. Despite the significant progress in these areas, a comprehensive approach that seamlessly integrates data and models is still lacking, leaving critical areas like microbiological research, drug discovery, and personalized medicine ripe for further exploration.

This Research Topic aims to spotlight innovative algorithms and computational tools that facilitate the integration of data and models, fostering transformative insights in bioinformatics and biomedical science. The primary goal is to highlight methodologies that bridge computational intelligence with critical bioinformatics challenges.

We seek research showcasing the power of computational intelligence strategies in the integration and analysis of multi-omics data, and multimodal biomedical information. Emphasis will be placed on contributions that explore the application of these computational methods in the fields of pangenomics, microbiology, drug discovery, and pharmacogenomics.

The scope of this Research Topic is concentrated on advancing our understanding of the intersection between computational tools and biomedical research. We invite a diverse range of articles focusing on, but not limited to, the following themes:
-Novel algorithms for analyzing single-omics or multi-omics data, with applications in bioinformatics, systems biology, and biomedicine
-Advanced methods for the integration of multimodal biomedical data, including molecular, imaging, and clinical datasets.
-Applications of AI and machine learning in bioinformatics for disease classification, biomarker discovery, and patient stratification
-Cutting-edge computational techniques in microbiological research, encompassing metagenomics, microbiome analysis, and antimicrobial resistance.
-Computational approaches for drug discovery and pharmacogenomics, targeting drug repositioning, response prediction, and target identification.

This Research Topic welcomes original research articles, methods and toolsets, hypotheses and theories, perspectives, and reviews.
Note: This topic is associated with CIBB 2025, but submissions from all authors are welcome

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Advanced Computational Approaches For Data And Model Integration In Bioinformatic and Biomedical Research