GAB_2024v15n4

Genomics and Applied Biology 2024, Vol.15, No.4, 172-181 http://bioscipublisher.com/index.php/gab 181 Narayan S., Liew Z., Bronstein J.M., and Ritz B., 2017, Occupational pesticide use and Parkinson's disease in the Parkinson environment gene (PEG) study, Environ Int., 107: 266-273. https://doi.org/10.1016/j.envint.2017.04.010 Nicora G., Vitali F., Dagliati A., Geifman N., and Bellazzi R., 2020, Integrated multi-omics analyses in oncology: a review of machine learning methods and tools, Frontiers in Oncology, 10: 1030. https://doi.org/10.3389/fonc.2020.01030 Pai S., and Bader G., 2018, Patient similarity networks for precision medicine, Journal of Molecular Biology, 430(18 Pt A): 2924-2938. https://doi.org/10.1016/j.jmb.2018.05.037 Raimundo F., Meng-Papaxanthos L., Vallot C., and Vert J., 2021, Machine learning for single-cell genomics data analysis, Current Opinion in Systems Biology, 26: 64-71. https://doi.org/10.1016/j.coisb.2021.04.006 Reel P., Reel S., Pearson E., Trucco E., and Jefferson E., 2021, Using machine learning approaches for multi-omics data analysis: A review, Biotechnology advances, 49: 107739. https://doi.org/10.1016/j.biotechadv.2021.107739 Ritchie M., Holzinger E., Li R., Pendergrass S., and Kim D., 2015, Methods of integrating data to uncover genotype-phenotype interactions, Nature Reviews Genetics, 16: 85-97. https://doi.org/10.1038/nrg3868 Roberts M., Fohner A., Landry L., Olstad D., Smit A., Turbitt E., and Allen C., 2021, Advancing precision public health using human genomics: examples from the field and future research opportunities, Genome Medicine, 13(1): 97. https://doi.org/10.1186/s13073-021-00911-0 Satam H., Joshi K., Mangrolia U., Waghoo S., Zaidi G., Rawool S., Thakare R., Banday S., Mishra A., Das G., and Malonia S., 2023, Next-generation sequencing technology: current trends and advancements, Biology, 12(7): 997. https://doi.org/10.3390/biology12070997 Tremblay M., and Rouleau G., 2017, Deep genealogical analysis of a large cohort of participants in the CARTaGENE project (Quebec, Canada), Ann Hum Biol., 44(4):357-365. https://doi.org/10.1080/03014460.2017.1300326 Watson D., 2021, Interpretable machine learning for genomics, Human Genetics, 141: 1499-1513. https://doi.org/10.1007/s00439-021-02387-9 Wishart D., 2016, Emerging applications of metabolomics in drug discovery and precision medicine, Nature Reviews Drug Discovery, 15: 473-484. https://doi.org/10.1038/nrd.2016.32 Woodahl E.L., Lesko L.J., Hopkins S., Robinson R.F., Thummel K.E., and Burke W., 2014, Pharmacogenetic research in partnership with American Indian and Alaska Native communities, Pharmacogenomics, 15(9): 1235-1241. https://doi.org/10.2217/pgs.14.91 Xiao Y., Liu H., Wu L., Warburton M., and Yan J., 2017, Genome-wide association studies in maize: praise and stargaze, Molecular Plant, 10(3): 359-374. https://doi.org/10.1016/j.molp.2016.12.008 Xu H., 2020, Big data challenges in genomics, Elsevier, 43: 337-348. https://doi.org/10.1016/bs.host.2019.08.002 Yelmen B., Decelle A., Boulos L., Szatkownik A., Furtlehner C., Charpiat G., and Jay F., 2023, Deep convolutional and conditional neural networks for large-scale genomic data generation, PLOS Computational Biology, 19(10): e1011584. https://doi.org/10.1371/journal.pcbi.1011584 Ziegler A., König I., and Thompson J., 2008, Biostatistical aspects of genome-wide association studies, Biometrical Journal, 50(1): 8-28. https://doi.org/10.1002/bimj.200710398

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