Computational Molecular Biology 2024, Vol.14, No.5, 211-219 http://bioscipublisher.com/index.php/cmb 214 Figure 1 The workflow of survival-related genes identification (Adopted from Zhao et al., 2020) Image caption: (A) Candidate survival-related gene screening. (B) Prognostic biomarker identifying (Adopted from Zhao et al., 2020) 4.3 Drug metabolism and toxicity prediction The integration of multi-omics data is crucial for predicting drug metabolism and toxicity, which are key factors in the development of safe and effective therapies. By combining genomic, transcriptomic, proteomic, and metabolomic data, researchers can gain a comprehensive understanding of the molecular mechanisms underlying drug response and adverse effects. This approach helps in identifying biomarkers for drug metabolism and toxicity, which can be used to tailor treatments to individual patients, thereby minimizing adverse effects and improving therapeutic outcomes. For instance, machine learning methods applied to multi-omics data have been shown to predict drug toxicity with high accuracy, facilitating the development of safer drugs (Reel et al., 2021; Karaman and Işik, 2023; Xiang and Wu, 2024). 5 Applications of Multi-Omics Integration in Agriculture 5.1 Multi-omics in crop breeding Multi-omics integration has revolutionized crop breeding by providing comprehensive insights into the genetic and phenotypic traits of crops. Advances in next-generation sequencing (NGS) have enabled the development of various omics technologies such as genomics, transcriptomics, proteomics, metabolomics, phenomics, and ionomics. These technologies have been instrumental in understanding the growth, senescence, yield, and responses to biotic and abiotic stresses in crops like wheat, soybean, tomato, barley, maize, millet, cotton, Medicago truncatula, and rice. The integration of functional genomics with other omics approaches has highlighted the relationships between crop genomes and phenotypes under specific physiological and environmental conditions, thereby enhancing crop breeding science (Yang et al., 2021). The integration of omics databases is crucial for selecting and developing new plant varieties with desirable traits such as increased yield, improved disease resistance, and enhanced nutritional value, which is essential for global food security (Chao et al., 2023).
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