Computational Molecular Biology 2024, Vol.14, No.5, 220-228 http://bioscipublisher.com/index.php/cmb 228 Subramanian I., Verma S., Kumar S., Jere A., and Anamika K., 2020, Multi-omics Data Integration Interpretation and Its application, Bioinformatics and Biology Insights, 14: 1177932219899051. https://doi.org/10.1177/1177932219899051 Sun Y.V., and Hu Y.J., 2016, Integrative analysis of multi-omics data for discovery and functional studies of complex human diseases, Advances in Genetics, 93: 147-190. https://doi.org/10.1016/bs.adgen.2015.11.004 Teschendorff A., and Relton C., 2017, Statistical and integrative system-level analysis of DNA methylation data, Nature Reviews Genetics, 19: 129-147. https://doi.org/10.1038/nrg.2017.86 Wang X.C., 2019, Protein and proteome atlas for plants under stresses: new highlights and ways for integrated omics in post-genomics era, International Journal of Molecular Sciences, 20(20): 5222. https://doi.org/10.3390/ijms20205222 Wörheide M., Krumsiek J., Kastenmüller G., and Arnold M., 2021, Multi-omics integration in biomedical research - A metabolomics-centric review, Analytica Chimica Acta, 1141: 144-162. https://doi.org/10.1016/j.aca.2020.10.038 Yang J., Fritsche L., Zhou X., and Abecasis G., 2017, A scalable bayesian method for integrating functional information in genome-wide association studies, American Journal of Human Genetics, 101(3): 404-416. https://doi.org/10.1016/j.ajhg.2017.08.002 Yang Y., Saand M.A., Huang L., Abdelaal W.B., Zhang J., Wu Y., Li J., Sirohi M.H., and Wang F., 2021, Applications of multi-omics technologies for crop improvement, Frontiers in Plant Science, 12: 563953. https://doi.org/10.3389/fpls.2021.563953 Zhu C., Zhang Y., Li Y.E., Lucero J., Behrens M.M., and Ren B., 2021, Joint profiling of histone modifications and transcriptome in single cells from mouse brain, Nature Methods, 18(3): 283-292. https://doi.org/10.1038/s41592-021-01060-3
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