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From Generic to Custom: A Survey on Role of Machine Learning in Pharmacogenomics, Its Applications and Challenges

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Evolution in Computational Intelligence

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1176))

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Abstract

Current advancements in medical sciences and pharmacogenomics are focusing on efficient, faster, and economic ways of drug delivery. On the other hand, big data analytics and machine learning are pushing the boundaries of human intelligence. Our aim is to bridge this gap between medical science and engineering by providing solutions that adhere to the requirements. Fields like remote robotic operations or artificial intelligence (AI) system for disease diagnosis and precision medicine are few that bridge this gap. Our proposed work in the field of precision medicine is an effort to contribute for making society healthier and more sustainable by reducing costs as well as reducing iatrogenic diseases by adopting technology advancements. The main focus of this survey paper is to understand the trends in field of biology, particularly in pharmacogenomics for better treatment of diseases by effective medication considering diabetes as an example. The paper also discusses issues related to application of machine learning in genomic data.

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References

  1. Adzhubei, I.A., et al.: A method and server for predicting damaging missense mutations. Nat. Methods 7, 248 EP (2010)

    Google Scholar 

  2. Albert, F.W., Kruglyak, L.: The role of regulatory variation in complex traits and disease. Nat. Rev. Genetics 16, 197 EP (2015)

    Google Scholar 

  3. Amarasingham, R., et al.: Clinical information technologies and inpatient outcomes: a multiple hospital study. Arch. Intern. Med. 169(2), 108–114 (2009)

    Article  Google Scholar 

  4. Bland, J.S., et al.: Translating emerging science into individualized wellness. Adv. Med. 2017, 1–5 (2017)

    Article  Google Scholar 

  5. Cheung, P.C., et al.: Childhood obesity incidence in the United States: a systematic review. Childhood Obes. (Print) 12(1), 1–11 (2016)

    Article  Google Scholar 

  6. Cong, L., et al.: Multiplex genome engineering using crispr/cas systems. Science 339(6121), 819–823 (2013)

    Article  Google Scholar 

  7. Cook, K.: Deep learning algorithms already hitting its limitations? (2019). https://www.houseofbots.com/news-detail/4463-1-deep-learning-algorithms-already-hitting-its-limitations

  8. Dermitzakis, E.T., et al.: Evolutionary discrimination of mammalian conserved non-genic sequences (cngs). Science 302(5647), 1033–1035 (2003)

    Article  Google Scholar 

  9. Evans, R.S.: Electronic health records: then, now, and in the future. Yearb. Med. Inform. 25(Suppl 1), S48–S61 (2016)

    Google Scholar 

  10. Farmer, A., Fox, R.: Diagnosis, classification, and treatment of diabetes. BMJ 342 (2011)

    Google Scholar 

  11. Gottesman, O., Kuivaniemi, H., Tromp, G., et al.: The electronic medical records and genomics (eMERGE) network: past, present, and future. Genet. Med. 15(10), 761–771 (2013)

    Article  Google Scholar 

  12. Harrow, J., Frankish, A., Gonzalez, E.A.: GENCODE: the reference human genome annotation for the ENCODE project. Genome Res. 22(9), 1760–1774 (2012)

    Article  Google Scholar 

  13. Hollands, G.J., et al.: The impact of communicating genetic risks of disease on risk-reducing health behaviour: systematic review with meta-analysis. BMJ (Clin. Res. Ed.) 352, i1102–i1102 (2016)

    Google Scholar 

  14. Huang, H., et al.: Predicting drug efficacy based on the integrated breast cancer pathway model. In: 2011 IEEE International Workshop on Genomic Signal Processing and Statistics (GENSIPS), pp. 42–45, Dec 2011

    Google Scholar 

  15. Kaveeshwar, S.A., Cornwall, J.: The current state of diabetes mellitus in India. Australas. Med. J. 7(1), 45–48 (2014)

    Article  Google Scholar 

  16. William, C., Knowler, M.D., et al.: Reduction in the incidence of type 2 diabetes with lifestyle intervention or metformin. N. Engl. J. Med. 346(6), 393–403 (2002)

    Article  Google Scholar 

  17. Ledford, H.: End of cancer atlas prompts rethink: Geneticists debate whether focus should shift from sequencing genomes to analysing function. Nature 157, 128–129 (2015)

    Article  Google Scholar 

  18. Lindblad-Toh, K., Garber, M., Zuk, O., Lin, M.F., Parker, B.J., et al.: A high-resolution map of human evolutionary constraint using 29 mammals. Nature 478(7370), 476–482 (2011)

    Article  Google Scholar 

  19. Marx, V.: The big challenges of big data. Nature 498, 255 EP (2013)

    Google Scholar 

  20. McCarty, C.A., et al.: The eMERGE Network: a consortium of biorepositories linked to electronic medical records data for conducting genomic studies. BMC Med. Genomics 4(1), 13 (2011)

    Article  Google Scholar 

  21. Meigs, J.B., et al.: Genotype score in addition to common risk factors for prediction of type 2 diabetes. N. Engl. J. Med. 359(21), 2208–2219 (2008)

    Article  Google Scholar 

  22. Mudasir, A.W., et al.: Big data: issues, challenges and techniques in business intelligence (2016)

    Google Scholar 

  23. Musunuru, K., et al.: Interdisciplinary models for research and clinical endeavors in genomic medicine: a scientific statement from the American Heart Association. Circ. Genomic Precis. Med. 11(6) (2018)

    Google Scholar 

  24. Ohno-Machado, L., et al.: Genomics and electronic health record systems. Hum. Mol. Genet. 27(R1), R48–R55 (2018)

    Article  Google Scholar 

  25. Oram, R.A., et al.: A type 1 diabetes genetic risk score can aid discrimination between type 1 and type 2 diabetes in young adults. Diab. Care 39(3), 337–344 (2016)

    Article  Google Scholar 

  26. Parente, S.T., McCullough, J.S.: Perspective: health information technology and patient safety: evidence from panel data. Health Aff. 28(2), 357–360 (2009)

    Article  Google Scholar 

  27. Pritchard, J.K., Przeworski, M.: Linkage disequilibrium in humans: models and data. Am. J. Hum. Genetics 69(1), 1–14 (2001)

    Article  Google Scholar 

  28. Rajkomar, A., et al.: Scalable and accurate deep learning for electronic health records. NPJ Digit. Med. 1, 1–10 (2018)

    Article  Google Scholar 

  29. Scott, R.A., et al.: Large-scale association analyses identify new loci influencing glycemic traits and provide insight into the underlying biological pathways. Nat. Genetics 44, 991 EP (2012)

    Google Scholar 

  30. Vassy, J.L., et al.: Polygenic type 2 diabetes prediction at the limit of common variant detection. Diabetes 63(6), 2172–2182 (2014)

    Article  Google Scholar 

  31. Visscher, P.M., et al.: Biology, function, and translation. Am. J. Hum. Genetics 101(1), 5–22 (2017)

    Article  Google Scholar 

  32. Xiong, H.Y., et al.: RNA splicing. The human splicing code reveals new insights into the genetic determinants of disease. Science 347(6218), 1254806–1254806 (2015) (New York, N.Y.)

    Google Scholar 

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Authors and Affiliations

  1. School of Computing and Information Technology, REVA University, Bangalore, India

    Sana Aiman & Kiran Kumari Patil

Authors
  1. Sana Aiman
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  2. Kiran Kumari Patil
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Corresponding author

Correspondence to Sana Aiman .

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Editors and Affiliations

  1. Department of Electronics and Communication Engineering, Shri Ramswaroop Memorial Group of Professional Colleges (SRMGPC), Lucknow, Uttar Pradesh, India

    Vikrant Bhateja

  2. Department of Computer Science and Information Engineering, National Dong Hwa University, Hualien, Taiwan

    Sheng-Lung Peng

  3. School of Computer Engineering, Kalinga Institute of Industrial Technology (KIIT), Bhubaneswar, Odisha, India

    Suresh Chandra Satapathy

  4. Department of Informatics, University of Leicester, Leicester, UK

    Yu-Dong Zhang

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Aiman, S., Patil, K.K. (2021). From Generic to Custom: A Survey on Role of Machine Learning in Pharmacogenomics, Its Applications and Challenges. In: Bhateja, V., Peng, SL., Satapathy, S.C., Zhang, YD. (eds) Evolution in Computational Intelligence. Advances in Intelligent Systems and Computing, vol 1176. Springer, Singapore. https://doi.org/10.1007/978-981-15-5788-0_4

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