Towards Precision Medicine with Genomics using Big Data Analytics
DOI:
https://doi.org/10.22399/ijcesen.906Keywords:
Big data Analytics, Medicine, Glycomics, Genomics, machine learningAbstract
Precision medicine is considered to be the future of healthcare. It allows doctors to select treatments based on the patient's genetic information. Precision medicine is being adapted to a few typical complicated treatments like cancer at an intermediate level.
As genetic information is in large volumes, Big data analytics showing a reliable promise of the modern-day health care revolution. Extremely large and continuous collection of large volumes of data like Genomics, Proteomics, Glycomics etc. is creating a challenge in analysis and interpretation, which is addressed effectively by the Big data analytics.
This research work reviews and highlights the evolution of Precision medicine, Big Data Analytics and its significance in Precision medicine and related work. Also detailed the Machine learning perspectives on the Precise medicine with genomic data models along with Challenges.
References
Hassan M, Awan FM, Naz A, deAndrés-Galiana EJ, Alvarez O, Cernea A, Fernández-Brillet L, Fernández-Martínez JL, Kloczkowski A. (2022). Innovations in Genomics and Big Data Analytics for Personalized Medicine and Health Care: A Review. Int J Mol Sci. 23(9):4645. doi: 10.3390/ijms23094645. DOI: https://doi.org/10.3390/ijms23094645
Hulsen Tim, Jamuar Saumya S., Moody Alan R., Karnes Jason H., Varga Orsolya, Hedensted Stine, Spreafico Roberto, Hafler David A., McKinney Eoin F. (2019). From Big Data to Precision Medicine, Frontiers in Medicine, DOI=10.3389/fmed.2019.00034
Cirillo, D.; Valencia, A. (2019). Big data analytics for personalized medicine. Curr. Opin. Biotechnol. 58, 161–167. DOI: https://doi.org/10.1016/j.copbio.2019.03.004
Ginsburg, G.S.; Willard, H.F. (2019). Genomic and personalized medicine: Foundations and applications. Transl. Res. 2009, 154, 277–287. DOI: https://doi.org/10.1016/j.trsl.2009.09.005
Naqvi, M.R.; Jaffar, M.A.; Aslam, M.; Shahzad, S.K.; Iqbal, M.W.; Farooq, A. (2020). Importance of big data in precision and personalized medicine. In Proceedings of the 2020 International Congress on Human-Computer Interaction, Optimization and Robotic Applications (HORA), Ankara, Turkey, 30 July 2020; pp. 1–6 DOI: https://doi.org/10.1109/HORA49412.2020.9152842
Iriart, J.A.B. (2019). Precision medicine/personalized medicine: A critical analysis of movements in the transformation of biomedicine in the early 21st century. Cadernos. Cad. De Saúde Publica 35.
Beckmann, J.S.; Lew, D. (2016) Reconciling evidence-based medicine and precision medicine in the era of big data: Challenges and opportunities. Genome Med. 8, 1–11. DOI: https://doi.org/10.1186/s13073-016-0388-7
Espinal-Enríquez, J.; Mejía-Pedroza, R.; Hernández-Lemus, E. (2017) Computational approaches in precision medicine. Progress and Challenges in Precision Medicine; pp. 233–250 DOI: https://doi.org/10.1016/B978-0-12-809411-2.00013-1
Hulsen, T.; Jamuar, S.; Moody, A.; Karnes, J.; Varga, O.; Hedensted, S.; Spreafico, R.; Hafler, D.; McKinney, E. (2019). From big data to precision medicine. Front. Med. 6, 34. DOI: https://doi.org/10.3389/fmed.2019.00034
Durinx, Christine, Jo McEntyre, Ron Appel, Rolf Apweiler, Mary Barlow, Niklas Blomberg, Chuck Cook et al. (2016) Identifying ELIXIR core data resources. F1000Research 5. DOI: https://doi.org/10.12688/f1000research.9656.1
Ronald Margolis, Leslie Derr, Michelle Dunn, Michael Huerta, Jennie Larkin, Jerry Sheehan, Mark Guyer, Eric D Green, (2014). The National Institutes of Health's Big Data to Knowledge (BD2K) initiative: capitalizing on biomedical big data, Journal of the American Medical Informatics Association, 21(6);957–958, https://doi.org/10.1136/amiajnl-2014-002974 DOI: https://doi.org/10.1136/amiajnl-2014-002974
Genomics Reimagined by Big Data & Analytics – 3AI, October 22, 2020, Genomics Reimagined by Big Data & Analytics – 3AI
Bibault, J.-E. (2020). Real-life clinical data mining: Generating hypotheses for evidence-based medicine. Ann. Transl. Med. 8, 69. DOI: https://doi.org/10.21037/atm.2019.10.99
Normandeau, K. (2013). Beyond Volume, Variety and Velocity is the Issue of Big Data Veracity. Inside Big Data https: //insidebigdata.com/2013/09/12/beyond-volume-variety-velocity-issue-big-data-veracity/ (accessed on 18 January 2022).
Gandomi, A.; Haider, M. (2015). Beyond the hype: Big data concepts, methods, and analytics. Int. J. Inf. Manag. 35, 137–144. DOI: https://doi.org/10.1016/j.ijinfomgt.2014.10.007
Diebold, F.X.; Cheng, X.; Diebold, S.; Foster, D.; Halperin, M.; Lohr, S.; Mashey, J.; Nickolas, T.; Pai, M.; Pospiech, M. (2012). A Personal Perspective on the Origin (s) and Development of “Big Data”: The Phenomenon, the Term, and the Discipline. DOI: https://doi.org/10.2139/ssrn.2202843
Auffray, C.; Balling, R.; Barroso, I.; Bencze, L.; Benson, M.; Bergeron, J.; Bernal-Delgado, E.; Blomberg, N.; Bock, C.; Conesa, A. (2016). Making sense of big data in health research: Towards an EU action plan. Genome Med. 8, 1–13. DOI: https://doi.org/10.1186/s13073-016-0323-y
Dash, S.; Shakyawar, S.K.; Sharma, M.; Kaushik, S. (2019). Big data in healthcare: Management, analysis and future prospects. J. Big Data 6, 54. DOI: https://doi.org/10.1186/s40537-019-0217-0
Fernandez Martinez, J.L.; Fernandez Muniz, M.Z.; Tompkins, M.J. (2012). On the topography of the cost functional in linear and nonlinear inverse problems. Geophysics 77, W1–W15. DOI: https://doi.org/10.1190/geo2011-0341.1
Fernández-Martínez, J.L.; Fernández-Muñiz, Z.; Pallero, J.; Pedruelo-González, L.M. (2013). From Bayes to Tarantola: New insights to understand uncertainty in inverse problems. J. Appl. Geophys. 98, 62–72. DOI: https://doi.org/10.1016/j.jappgeo.2013.07.005
Fernández-Martínez, J.L.; Pallero, J.; Fernández-Muñiz, Z.; Pedruelo-González, L.M. (2014) The effect of noise and Tikhonov’s regularization in inverse problems. Part I: The linear case. J. Appl. Geophys. 108, 176–185. DOI: https://doi.org/10.1016/j.jappgeo.2014.05.006
Fernández-Martínez, J.L.; Pallero, J.; Fernández-Muñiz, Z.; Pedruelo-González, L.M. (2014). The effect of noise and Tikhonov’s regularization in inverse problems. Part II: The nonlinear case. J. Appl. Geophys. 108, 186–193. DOI: https://doi.org/10.1016/j.jappgeo.2014.05.005
Zhang, H. (2016) Overview of sequence data formats. In Statistical Genomics; Springer: Berlin/Heidelberg, Germany, pp. 3–17. DOI: https://doi.org/10.1007/978-1-4939-3578-9_1
Lek, M.; Karczewski, K.; Minikel, E.; Samocha, K.; Banks, E.; Fennell, T.; O’Donnell-Luria, A.; Ware, J.; Hill, A.; Cummings, B.; et al. (2016). Analysis of protein-coding genetic variation in 60,706 humans. Nature, 536, 285–291. DOI: https://doi.org/10.1038/nature19057
Peter Goodhand et al. The Global Alliance for Genomics and Health. A federated ecosystem for sharing genomic, clinical data. Science, 352, 1278–1280. DOI: 10.1126/science.aaf6162 DOI: https://doi.org/10.1126/science.aaf6162
Wenger, A.M.; Guturu, H.; Bernstein, J.A.; Bejerano, G. (2017). Systematic reanalysis of clinical exome data yields additional diagnoses: Implications for providers. Genet. Med., 19,209–214. DOI: https://doi.org/10.1038/gim.2016.88
Wright, C.F.; McRae, J.F.; Clayton, S.; Gallone, G.; Aitken, S.; FitzGerald, T.W.; Jones, P.; Prigmore, E.; Rajan, D.; Lord, J. (2018). Making new genetic diagnoses with old data: Iterative reanalysis and reporting from genome-wide data in 1133 families with developmental disorders. Genet. Med. 20, 1216–1223. DOI: https://doi.org/10.1038/gim.2017.246
Anuradha, T., Lakshmi Surekha, T., Nuthakki, P., Domathoti, B., Ghorai, G., Shami, F.A. (2022) Graph theory algorithms of Hamiltonian cycle from quasi-spanning tree and domination based on vizing conjecture. J. Math. https://doi.org/10.1155/2022/1618498 DOI: https://doi.org/10.1155/2022/1618498
Sekhar, J. N. C., Domathoti, B., & Santibanez Gonzalez, E. D. R. (2023). Prediction of Battery Remaining Useful Life Using Machine Learning Algorithms. Sustainability, 15(21), 15283. https://doi.org/10.3390/su152115283 DOI: https://doi.org/10.3390/su152115283
L. Hood and N. D. Price, (2014). Demystifying disease, democratizing health care, Science Translational Medicine, 6,225, Article ID 225ed5, 2014.
J. W. Davey, P. A. Hohenlohe, P. D. Etter, J. Q. Boone, J. M. Catchen, and M. L. Blaxter, (2011). Genome-wide genetic marker discovery and genotyping using next-generation sequencing, Nature Reviews Genetics, 12(7);499–510. DOI: https://doi.org/10.1038/nrg3012
T. J. Treangen and S. L. Salzberg, (2012). Repetitive DNA and next generation sequencing: computational challenges and solutions, Nature Reviews Genetics, 13(1);36–46. DOI: https://doi.org/10.1038/nrg3117
D. C. Koboldt, K. M. Steinberg, D. E. Larson, R. K. Wilson, and E. R. Mardis, (2013). The next-generation sequencing revolution and its impact on genomics, Cell, 155(1);27–38. DOI: https://doi.org/10.1016/j.cell.2013.09.006
E. S. Lander, L. M. Linton, B. Birren et al., (2001). Initial sequencing and analysis of the human genome, Nature, 409(6822);860–921.
R. Drmanac, A. B. Sparks, M. J. Callow et al., (2010). Human genome sequencing using unchained base reads on self-assembling DNA nanoarrays, Science, 327(5961);78–81.
R. Chen, G. I. Mias, J. Li-Pook-Than et al., (2012) Personal omics profiling reveals dynamic molecular and medical phenotypes, Cell, 148(6);1293–1307, DOI: https://doi.org/10.1016/j.cell.2012.02.009
Institute of Medicine, Informatics Needs and Challenges in Cancer Research: Workshop Summary, The National Academies Press, Washington, DC, USA, 2012.
E. M. van Allen, N. Wagle, and M. A. Levy, (2013) Clinical analysis and interpretation of cancer genome data, Journal of Clinical Oncology, 31(15);1825–1833. DOI: https://doi.org/10.1200/JCO.2013.48.7215
A. Tabchy, C. X. Ma, R. Bose, and M. J. Ellis, (2013) Incorporating genomics into breast cancer clinical trials and care, Clinical Cancer Research, 19(23);6371–6379. DOI: https://doi.org/10.1158/1078-0432.CCR-13-0837
F. Andre, E. Mardis, M. Salm, J. C. Soria, L. L. Siu, and C. Swanton, (2014). Prioritizing targets for precision cancer medicine, Annals of Oncology, 25(12);2295–2303. DOI: https://doi.org/10.1093/annonc/mdu478
G. Karlebach and R. Shamir, (2008). Modelling and analysis of gene regulatory networks, Nature Reviews Molecular Cell Biology, 9(10);770–780. DOI: https://doi.org/10.1038/nrm2503
J. Loven, D. A. Orlando, A. A. Sigova et al., (2012). Revisiting global ´ gene expression analysis, Cell, 151(3);476–482. DOI: https://doi.org/10.1016/j.cell.2012.10.012
J. T. Leek, R. B. Scharpf, H. C. Bravo et al., (2010) Tackling the widespread and critical impact of batch effects in high throughput data, Nature Reviews Genetics, 11(10);733–739. DOI: https://doi.org/10.1038/nrg2825
P. Khatri, M. Sirota, and A. J. Butte, (2012). Ten years of pathway analysis: current approaches and outstanding challenges, PLoS Computational Biology, 8(2);e1002375. DOI: https://doi.org/10.1371/journal.pcbi.1002375
P. Khatri, S. Draghici, G. C. Ostermeier, and S. A. Krawetz, (2001). Profiling gene expression using Onto-Express, Genomics, 79(2);266–270. DOI: https://doi.org/10.1006/geno.2002.6698
S. Draghici, P. Khatri, R. P. Martins, G. C. Ostermeier, and S. A. Krawetz, (2003). Global functional profiling of gene expression, Genomics, 81(2)98–104. DOI: https://doi.org/10.1016/S0888-7543(02)00021-6
D. W. Huang, B. T. Sherman, and R. A. Lempicki, (2009). Bioinformatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists, Nucleic Acids Research, 37(1);1–13. DOI: https://doi.org/10.1093/nar/gkn923
B. R. Zeeberg, W. Feng, G. Wang et al., (2003). GoMiner: a resource for biological interpretation of genomic and proteomic data, Genome Biology, 4(4);R28, DOI: https://doi.org/10.1186/gb-2003-4-4-r28
G. Bindea, B. Mlecnik, H. Hackl et al. (2009)., Cluego: a cytoscape plug-in to decipher functionally grouped gene ontology and pathway annotation networks, Bioinformatics, 25(8);1091–1093 DOI: https://doi.org/10.1093/bioinformatics/btp101
A. Subramanian, P. Tamayo, V. K. Mootha et al., (2005). Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles, Proceedings of the National Academy of Sciences of the United States of America, 102(43);15545–15550. DOI: https://doi.org/10.1073/pnas.0506580102
S. Draghici, P. Khatri, A. L. Tarca et al., (2007). A systems biology approach for pathway level analysis, Genome Research, 17(10);1537–1545. DOI: https://doi.org/10.1101/gr.6202607
I. Thiele, N. Swainston, R. M. T. Fleming et al., (2013). A communitydriven global reconstruction of human metabolism, Nature Biotechnology, 31(5);419–425. DOI: https://doi.org/10.1038/nbt.2488
D. Marbach, J. C. Costello, R. Kuffner et al.,(2012). Wisdom of crowds ¨ for robust gene network inference, Nature Methods, 9(8);796–804. DOI: https://doi.org/10.1038/nmeth.2016
R.-S.Wang, A. Saadatpour, and R. Albert, (2012). Boolean modeling in systems biology: an overview of methodology and applications, Physical Biology, 9(5);055001. DOI: https://doi.org/10.1088/1478-3975/9/5/055001
W. Gong, N. Koyano-Nakagawa, T. Li, and D. J. Garry, (2015). Inferring dynamic gene regulatory networks in cardiac differentiation through the integration of multi-dimensional data, BMC Bioinformatics, 16(1);74. DOI: https://doi.org/10.1186/s12859-015-0460-0
K. C. Chen, L. Calzone, A. Csikasz-Nagy, F. R. Cross, B. Novak, and J. J. Tyson, (2004). Integrative analysis of cell cycle control in budding yeast, Molecular Biology of the Cell, 15(8);3841–3862. DOI: https://doi.org/10.1091/mbc.e03-11-0794
S. Kimura, K. Ide, A. Kashihara et al., (2005). Inference of S-system models of genetic networks using a cooperative coevolutionary algorithm, Bioinformatics, 21(7);1154–1163. DOI: https://doi.org/10.1093/bioinformatics/bti071
J. Gebert, N. Radde, and G.-W. Weber, (2007). Modeling gene regulatory networks with piecewise linear differential equations, European Journal of Operational Research, 181(3);1148–1165. DOI: https://doi.org/10.1016/j.ejor.2005.11.044
J. N. Bazil, K. D. Stamm, X. Li et al., (2014). The inferred cardiogenic gene regulatory network in the mammalian heart, PLoS ONE, 9(6);e100842. DOI: https://doi.org/10.1371/journal.pone.0100842
B. O. Palsson, Systems Biology, Cambridge University Press, 2006. DOI: https://doi.org/10.1017/CBO9780511790515
D. Marbach, R. J. Prill, T. Schaffter, C. Mattiussi, D. Floreano, and G. Stolovitzky, (2010). Revealing strengths and weaknesses of methods for gene network inference, Proceedings of the National Academy of Sciences of the United States of America, 107(14)6286–6291. DOI: https://doi.org/10.1073/pnas.0913357107
N. C. Duarte, S. A. Becker, N. Jamshidi et al.,(2007) Global reconstruction of the human metabolic network based on genomic and bibliomic data, Proceedings of the National Academy of Sciences of the United States of America, 104(6);1777–1782. DOI: https://doi.org/10.1073/pnas.0610772104
K. Raman and N. Chandra, (2009). Flux balance analysis of biological systems: applications and challenges, Briefings in Bioinformatics, 10(4);435–449. DOI: https://doi.org/10.1093/bib/bbp011
C. S. Henry, M. Dejongh, A. A. Best, P. M. Frybarger, B. Linsay, and R. L. Stevens, (2010). High-throughput generation, optimization and analysis of genome-scale metabolic models, Nature Biotechnology, 28(9);977–982. DOI: https://doi.org/10.1038/nbt.1672
K. Radrich, Y. Tsuruoka, P. Dobson et al., (2010). Integration of metabolic databases for the reconstruction of genome-scale metabolic networks, BMC Systems Biology, 4,114. DOI: https://doi.org/10.1186/1752-0509-4-114
K. Yizhak, T. Benyamini, W. Liebermeister, E. Ruppin, and T. Shlomi, (2010). Integrating quantitative proteomics and metabolomics with a genome-scale metabolic network model, Bioinformatics, 26(12);i255–i260. DOI: https://doi.org/10.1093/bioinformatics/btq183
C. R. Haggart, J. A. Bartell, J. J. Saucerman, and J. A. Papin, (2011). Whole-genome metabolic network reconstruction and constraint-based modeling, in Methods in Systems Biology, M. Verma, D. Jameson, and H. V. Westerhoff, Eds., vol. 500 of Methods in Enzymology, chapter 21, pp. 411–433, Academic Press. DOI: https://doi.org/10.1016/B978-0-12-385118-5.00021-9
D. McCloskey, B. Ø. Palsson, and A. M. Feist, (2013). Basic and applied uses of genome-scale metabolic network reconstructions of Escherichia coli, Molecular Systems Biology, 9,661. DOI: https://doi.org/10.1038/msb.2013.18
E. P. Gianchandani, A. K. Chavali, and J. A. Papin, (2010). The application of flux balance analysis in systems biology, Wiley Interdisciplinary Reviews: Systems Biology and Medicine, 2(3);372–382. DOI: https://doi.org/10.1002/wsbm.60
N. E. Lewis, H. Nagarajan, and B. O. Palsson, (2012). Constraining the metabolic genotype-phenotype relationship using a phylogeny of in silico methods, Nature Reviews Microbiology, 10(4);291–305. DOI: https://doi.org/10.1038/nrmicro2737
W. Zhang, F. Li, and L. Nie, (2010). Integrating multiple ‘omics’ analysis for microbial biology: application and methodologies, Microbiology, 156(2);287–301. DOI: https://doi.org/10.1099/mic.0.034793-0
A. S. Blazier and J. A. Papin, (2012). Integration of expression data in genome-scale metabolic network reconstructions, Frontiers in Physiology, 3,299. DOI: https://doi.org/10.3389/fphys.2012.00299
P. A. Jensen and J. A. Papin, (2011). Functional integration of a metabolic network model and expression data without arbitrary thresholding, Bioinformatics, 27(4);541–547. DOI: https://doi.org/10.1093/bioinformatics/btq702
R. L. Chang, L. Xie, L. Xie, P. E. Bourne, and B. Ø. Palsson, (2010). Drug off-target effects predicted using structural analysis in the context of a metabolic network model, PLoS Computational Biology, 6(9);e1000938. DOI: https://doi.org/10.1371/journal.pcbi.1000938
V. A. Huynh-Thu, A. Irrthum, L. Wehenkel, and P. Geurts, (2010). Inferring regulatory networks from expression data using tree-based methods, PLoS ONE, 5(9);e12776. DOI: https://doi.org/10.1371/journal.pone.0012776
R. Kuffner, T. Petri, P. Tavakkolkhah, L. Windhager, and R. (2012). Zimmer, “Inferring gene regulatory networks by ANOVA, Bioinformatics, 28(10);1376–1382. DOI: https://doi.org/10.1093/bioinformatics/bts143
R. J. Prill, J. Saez-Rodriguez, L. G. Alexopoulos, P. K. Sorger, and G. Stolovitzky, (2011). Crowdsourcing network inference: the dream predictive signaling network challenge, Science Signaling, 4;189. DOI: https://doi.org/10.1126/scisignal.2002212
T. Saithong, S. Bumee, C. Liamwirat, and A. Meechai, (2012). Analysis and practical guideline of constraint-based boolean method in genetic network inference, PLoS ONE, 7(1);e30232. DOI: https://doi.org/10.1371/journal.pone.0030232
S. Martin, Z. Zhang, A. Martino, and J.-L. Faulon, (2007). Boolean dynamics of genetic regulatory networks inferred from microarray time series data, Bioinformatics, 23(7);866–874. DOI: https://doi.org/10.1093/bioinformatics/btm021
J. N. Bazil, F. Qi, and D. A. Beard, (2011). A parallel algorithm for reverse engineering of biological networks, Integrative Biology, 3(12);1215–1223. DOI: https://doi.org/10.1039/c1ib00117e
O. Folger, L. Jerby, C. Frezza, E. Gottlieb, E. Ruppin, and T. Shlomi, (2011). Predicting selective drug targets in cancer through metabolic networks, Molecular Systems Biology, 7(1). DOI: https://doi.org/10.1038/msb.2011.63
Atmika Sharma, (2016). Big Data: A Healthcare Revolution, International Research Journal of Engineering and Technology (IRJET), 03(08).
L. Hood and S. H. Friend, (2011). Predictive, personalized, preventive, participatory (P4) cancer medicine, Nature Reviews Clinical Oncology, 8(3);184–187. DOI: https://doi.org/10.1038/nrclinonc.2010.227
L. Hood and M. Flores, (2012). A personal view on systems medicine and the emergence of proactive P4 medicine: predictive, preventive, personalized and participatory, New Biotechnology, 29(6);613–624. DOI: https://doi.org/10.1016/j.nbt.2012.03.004
L. Hood and N. D. Price, (2014). Demystifying disease, democratizing health care, Science Translational Medicine, 6(225);225ed5. DOI: https://doi.org/10.1126/scitranslmed.3008665
Anil, G. R. and Salman Abdul Moiz. (2019). Blockchain Enabled Smart Learning Environment Framework. Learning and Analytics in Intelligent Systems. DOI: https://doi.org/10.1007/978-3-030-24318-0_83
Van Loenen, B.; Kulk, S.; Ploeger, H. (2016). Data protection legislation: A very hungry caterpillar: The case of mapping data in the European Union. Gov. Inf. Q. 33, 338–345. DOI: https://doi.org/10.1016/j.giq.2016.04.002
M. Venkateswarlu, K. Thilagam, R. Pushpavalli, B. Buvaneswari, Sachin Harne, & Tatiraju.V.Rajani Kanth. (2024). Exploring Deep Computational Intelligence Approaches for Enhanced Predictive Modeling in Big Data Environments. International Journal of Computational and Experimental Science and Engineering, 10(4). https://doi.org/10.22399/ijcesen.676 DOI: https://doi.org/10.22399/ijcesen.676
K.S. Praveenkumar, & R. Gunasundari. (2025). Optimizing Type II Diabetes Prediction Through Hybrid Big Data Analytics and H-SMOTE Tree Methodology. International Journal of Computational and Experimental Science and Engineering, 11(1). https://doi.org/10.22399/ijcesen.727 DOI: https://doi.org/10.22399/ijcesen.727
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