Computational Molecular Biology 2024, Vol.14, No.5, 211-219 http://bioscipublisher.com/index.php/cmb 217 monitoring and analysis of biological processes as they occur. This approach leverages high-throughput sequencing technologies and bioinformatics tools to provide immediate insights into the interactions between various biomolecules. For instance, genome-resolved metagenomic approaches have been successfully applied in water engineering to link microbial community dynamics with system performance in real-time, facilitating more sustainable bioprocesses (McDaniel et al., 2021). The integration of real-time data from genomics, transcriptomics, proteomics, and metabolomics can enhance our understanding of complex biological systems and improve predictive models for various applications, including disease management and environmental monitoring. 8.2 AI and multi-omics integration Artificial intelligence (AI) and machine learning (ML) are poised to revolutionize multi-omics integration by providing robust analytical frameworks capable of handling the complexity and volume of omics data. AI techniques can effectively interpret multi-omics data, offering meaningful predictions and insights that traditional methods may miss. In horticultural research, for example, the integration of multi-omics data with AI has shown promise in advancing plant phenotyping, predictive breeding, and sustainable crop management (Cembrowska-Lech et al., 2023). The use of AI in multi-omics integration can also facilitate the identification of biomarkers, disease subtyping, and the development of personalized medicine approaches (Abdullah-Zawawi et al., 2022). As computational tools and high-performance computing resources continue to evolve, the potential for AI-driven multi-omics integration will expand, offering new opportunities for scientific discovery and innovation. 8.3 Multi-omics applications in ecology and environmental research The application of multi-omics technologies in ecology and environmental research is rapidly growing, providing comprehensive insights into the interactions between organisms and their environments. Multi-omics approaches have been employed to study microbial communities in various environmental settings, such as water engineering systems, where they have helped link microbial dynamics to process parameters and system performance. In plant systems, multi-omics integration has been used to understand the responses of crops to biotic and abiotic stresses, aiding in crop improvement and sustainable agriculture (Jamil et al., 2020; Yang et al., 2021). These technologies enable researchers to dissect complex ecological interactions and develop strategies for environmental conservation and resource management. The continued development and application of multi-omics in ecology and environmental research will enhance our ability to address global challenges related to biodiversity, climate change, and ecosystem health (Veenstra, 2020). Acknowledgments We appreciate colleague Chen.M. help in collecting literature and participating in discussions during the research process. Conflict of Interest Disclosure The authors affirm that this research was conducted without any commercial or financial relationships that could be construed as a potential conflict of interest. References Abdullah-Zawawi M.R., Govender N., Harun S., Muhammad N.A.N., Zainal Z., and Mohamed-Hussein Z., 2022 Multi-omics approaches and resources for systems-level gene function prediction in the plant kingdom, Plants, 11(19): 2614. https://doi.org/10.3390/plants11192614 Cembrowska-Lech D., Krzemińska A., Miller T., Nowakowska A., Adamski C., Radaczyńska M., Mikiciuk G., and Mikiciuk M., 2023, An integrated multi-omics and artificial intelligence framework for advance plant phenotyping in horticulture, Biology, 12(10): 1298. https://doi.org/10.3390/biology12101298. Chao H.Y., Zhang S.L., Hu Y.M., Ni Q.Y., Xin S.G., Zhao L., Ivanisenko V.A., Orlov Y.L., and Chen M., 2023, Integrating omics databases for enhanced crop breeding, Journal of Integrative Bioinformatics, 20(4): 20230012. https://doi.org/10.1515/jib-2023-0012 Dahal S., Yurkovich J.T., Xu H., Palsson B.O., and Yang L., 2020, Synthesizing systems biology knowledge from omics using genome‐scale models, Proteomics, 20(17-18): 1900282. https://doi.org/10.1002/pmic.201900282 Dhillon B.K., Smith M., Baghela A., Lee A.H.Y., and Hancock R.E.W., 2020, Systems biology approaches to understanding the human immune system, Frontiers in Immunology, 11: 1683. https://doi.org/10.3389/fimmu.2020.01683
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