Computational Molecular Biology 2024, Vol.14, No.3, 97-105 http://bioscipublisher.com/index.php/cmb 104 Ebrahim A., Brunk E., Tan J., O'Brien E.J., Kim D., Szubin R., Lerman J., Lechner A., Sastry A., Bordbar A., Feist A., and Palsson B., 2016, Multi-omic data integration enables discovery of hidden biological regularities, Nature Communications, 7(1): 13091. https://doi.org/10.1038/ncomms13091 Galetsi P., Katsaliaki K., and Kumar S., 2019, Values, challenges and future directions of big data analytics in healthcare: a systematic review, Social Science & Medicine, 241: 112533. https://doi.org/10.1016/j.socscimed.2019.112533 Gligorijević V., and Przulj N., 2015, Methods for biological data integration: perspectives and challenges, Journal of The Royal Society Interface, 12(112): 20150571. https://doi.org/10.1098/rsif.2015.0571 Goh W.W.B., and Wong L., 2020, The birth of bio-data science: trends, expectations, and applications, Genomics, Proteomics & Bioinformatics, 18(1): 5-15. https://doi.org/10.1016/j.gpb.2020.01.002 Greene C.S., Tan J.H., Ung M., Moore J., and Cheng C., 2014, Big Data bioinformatics, Journal of Cellular Physiology, 229(12): 1896-1900. https://doi.org/10.1002/jcp.24662 Gutierrez D., Gant-Branum R., Romer C., Farrow M., Allen J., Dahal N., Nei Y., Codreanu S., Jordan A., Palmer L., Sherrod S., McLean J., Skaar E., Norris J., and Caprioli R., 2018, An integrated, high-throughput strategy for multiomic systems level analysis, Journal of Proteome Research, 17(10): 3396-3408. https://doi.org/10.1021/acs.jproteome.8b00302 He K.Y., Ge D., and He M.M., 2017, Big data analytics for genomic medicine, International Journal of Molecular Sciences, 18(2): 412. https://doi.org/10.3390/ijms18020412 Hien L., Van N., Oanh K.T.P., Ton N.D., Hue H.T.T., Duong N.T., Hằng P.L.B., and Ha N.H., 2021, Genomics and big data: research, development and applications, Vietnam Journal of Biotechnology. 19(3): 393-410. https://doi.org/10.15625/1811-4989/16158 Ienca M., Ferretti A., Hurst S., Puhan M., Lovis C., and Vayena E., 2018, Considerations for ethics review of big data health research: a scoping review, PLoS ONE, 13(10): e0204937. https://doi.org/10.1371/journal.pone.0204937 Jan B., Farman H., Khan M., Imran M., Islam I., Ahmad A., Ali S., and Jeon G., 2017, Deep learning in big data analytics: a comparative study, Comput. Electr. Eng., 75: 275-287. https://doi.org/10.1016/J.COMPELECENG.2017.12.009 JaseenaK U., and Kovoor B., 2018, A survey on deep learning techniques for big data in biometrics, International Journal of Advanced Research in Computer Science, 9: 12-17. https://doi.org/10.26483/IJARCS.V9I1.5136 Jin S.T., Zeng X.X., Xia F., Huang W., and Liu X.R., 2020, Application of deep learning methods in biological networks, Briefings in Bioinformatics, 22(2): 1902-1917. https://doi.org/10.1093/bib/bbaa043 Juan H.F., and Huang H.C., 2023, Quantitative analysis of high‐throughput biological data, Wiley Interdisciplinary Reviews: Computational Molecular Science, 13(4): e1658. https://doi.org/10.1002/wcms.1658 Kamal M., Ripon S., Dey N., Ashour A., and Santhi V., 2016, A MapReduce approach to diminish imbalance parameters for big deoxyribonucleic acid dataset, Computer Methods and Programs in Biomedicine, 131: 191-206. https://doi.org/10.1016/j.cmpb.2016.04.005 Kashyap H., Ahmed H.A., Hoque N., Roy S., and Bhattacharyya D.K., 2015, Big data analytics in bioinformatics: a machine learning perspective, Arxiv preprint arxiv, 2015: 1506.05101. Kumar D., Bansal G., Narang A., Basak T., Abbas T., and Dash D., 2016, Integrating transcriptome and proteome profiling: strategies and applications, Proteomics, 16(19): 2533-2544. https://doi.org/10.1002/pmic.201600140 Li Y., and Chen L., 2014, Big biological data: challenges and opportunities, Genomics, Proteomics & Bioinformatics, 12(5): 187-189. https://doi.org/10.1016/j.gpb.2014.10.001 Libbrecht M., and Noble W., 2015, Machine learning applications in genetics and genomics, Nature Reviews Genetics, 16: 321-332. https://doi.org/10.1038/nrg3920 Mardis E., 2016, The challenges of big data, Disease Models & Mechanisms, 1(2): 293-314. https://doi.org/10.1242/dmm.025585 Miao Z., Humphreys B., McMahon A., and Kim J., 2021, Multi-omics integration in the age of million single-cell data, Nature Reviews Nephrology, 17: 710-724. https://doi.org/10.1038/s41581-021-00463-x Najafabadi M.M., Villanustre F., Khoshgoftaar T.M., Seliya N., Wald R., and Muharemagic E., 2015, Deep learning applications and challenges in big data analytics, Journal of Big Data, 2: 1-21. https://doi.org/10.1186/s40537-014-0007-7 O'Driscoll A., Daugelaite J., and Sleator R., 2013, 'Big data', hadoop and cloud computing in genomics, Journal of Biomedical Informatics, 46(5): 774-781. https://doi.org/10.1016/j.jbi.2013.07.001
RkJQdWJsaXNoZXIy MjQ4ODYzNA==