GAB_2026v17n5

Genomics and Applied Biology 2026, Vol.17, No.5, 312-325 http://bioscipublisher.com/index.php/gab 325 Noor F., Qamar M.T.U., Ashfaq U., Albutti A., Alwashmi A.S., and Aljasir M., 2022, Network pharmacology approach for medicinal plants: review and assessment, Pharmaceuticals, 15(5): 572. https://doi.org/10.3390/ph15050572 Pant P., Pandey S., and Dall'Acqua S., 2021, The influence of environmental conditions on secondary metabolites in medicinal plants: a literature review, Chemistry and Biodiversity, 18(11): e2100345. https://doi.org/10.1002/cbdv.202100345 Prajapati P., Shrivastav P., Prajapati J., and Prajapati B.G., 2025, Deep learning approaches for predicting bioactivity of natural compounds, The Natural Products Journal, 16(3): e22103155332267. https://doi.org/10.2174/0122103155332267241122143118 Rao X., and Liu W., 2025, A guide to metabolic network modeling for plant biology, Plants, 14(3): 484. https://doi.org/10.3390/plants14030484 Reinhardt J.K., Craft D., and Weng J.-K., 2025, Toward an integrated omics approach for plant biosynthetic pathway discovery in the age of AI, Trends in Biochemical Sciences, 50(4): 311-321. https://doi.org/10.1016/j.tibs.2025.01.010 Roopashree S., Anitha J., Challa S., Mahesh T., Venkatesan V.K., and Guluwadi S., 2024, Mapping of soil suitability for medicinal plants using machine learning methods, Scientific Reports, 14(1): 3741. https://doi.org/10.1038/s41598-024-54465-3 Shan Z., Ye J., Hao D., Xiao P., Chen Z., and Lu A., 2022, Distribution patterns and industry planning of commonly used traditional Chinese medicinal plants in China, Plant Diversity, 44(3): 255-261. https://doi.org/10.1016/j.pld.2021.11.003 Singh K.S., van der Hooft J.J.J., van Wees S.M., and Medema M.H., 2022, Integrative omics approaches for biosynthetic pathway discovery in plants, Natural Product Reports, 39(9): 1876-1896. https://doi.org/10.1039/D2NP00032F Srivastava P., and Bharadvaja N., 2026, Role of artificial intelligence in enhancing medicinal plant metabolites, 2026 2nd international conference on cognitive computing in engineering, communications, Sciences and Biomedical Health Informatics (IC3ECSBHI), 2026: 48-53. https://doi.org/10.1109/IC3ECSBHI67834.2026.11469148 Vigneault P., Lafond-Lapalme J., Deshaies A., Khun K., de La Sablonnière S., Filion M., Longchamps L., and Mimee B., 2024, An integrated data-driven approach to monitor and estimate plant-scale growth using UAV, ISPRS Open Journal of Photogrammetry and Remote Sensing, 11: 100052. https://doi.org/10.1016/j.ophoto.2023.100052 Wu X., Zhong L., Ding R., Wang C., Chen H., Zhong S., and Gu R., 2025, Non-destructive estimation of SPAD and biomass in Lamiophlomis rotata using hyperspectral imaging and deep learning with DRSA-CARS feature selection, Frontiers in Plant Science, 16: 1640779. https://doi.org/10.3389/fpls.2025.1640779 Xue H.T., Stanley-Baker M., Kong A.W.K., Li H.L., and Goh W.W.B., 2022, Data considerations for predictive modeling applied to the discovery of bioactive natural products, Drug Discovery Today, 27(8): 2235-2243. https://doi.org/10.1016/j.drudis.2022.05.009 Zhang A., Guan H., Dong Z., Jia X., Xue Y., Han F., Meng L., Yu X., Wang X., and Cao Y., 2025, Integrated diagnostics and time series sensitivity assessment for growth monitoring of a medicinal plant (Glycyrrhiza uralensis Fisch.) based on unmanned aerial vehicle multispectral sensors, Frontiers in Plant Science, 16: 1612898. https://doi.org/10.3389/fpls.2025.1612898

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