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Artificial Intelligence and Personalized Medicine

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Book cover Precision Medicine in Cancer Therapy

Part of the book series: Cancer Treatment and Research ((CTAR,volume 178))

Abstract

The development of high-throughput, data-intensive biomedical research assays and technologies has created a need for researchers to develop strategies for analyzing, integrating, and interpreting the massive amounts of data they generate. Although a wide variety of statistical methods have been designed to accommodate ‘big data,’  experiences with the use of artificial intelligence (AI) techniques suggest that they might be particularly appropriate. In addition,  the results of the application of these assays reveal a great heterogeneity in the pathophysiologic factors and processes that contribute to disease, suggesting that there is a need to tailor, or ‘personalize,’ medicines to the nuanced and often unique features possessed by individual patients. Given how important data-intensive assays are to revealing appropriate intervention targets and strategies for  treating an individual with a disease, AI can play an important role in the development of personalized medicines. We describe many areas where AI can play such a role and argue that AI’s ability to advance personalized medicine will depend critically on not only the refinement of relevant assays, but also on ways of storing, aggregating, accessing, and ultimately integrating, the data they produce. We also point out the limitations of many AI techniques in developing personalized medicines as well as consider areas for further research.

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References

  1. Russell S, Norvig P (2009) Artificial intelligence: a modern approach, 3rd edn. Pearson, Carmel, IN

    Google Scholar 

  2. Mahmud M et al (2018) Applications of deep learning and reinforcement learning to biological data. IEEE Trans Neural Netw Learn Syst 29(6):2063–2079

    Article  PubMed  Google Scholar 

  3. Webb S (2018) Deep learning for biology. Nature 554(7693):555–557

    Article  CAS  PubMed  Google Scholar 

  4. Fleming N (2018) How artificial intelligence is changing drug discovery. Nature 557(7707):S55–S57

    Article  CAS  PubMed  Google Scholar 

  5. Committee to Review the Clinical and Translational Science Awards Program at the National Center for Advancing Translational Sciences, Board on Health Sciences Policy, Institute of Medicine, Leshner AI, Terry S (eds) (2013) The CTSA program at NIH: opportunities for advancing clinical and translational research. The national academies collection: reports funded by National Institutes of Health. National Academies Press, Washington, DC

    Google Scholar 

  6. Schork NJ, Nazor K (2017) Integrated genomic medicine: a paradigm for rare diseases and beyond. Adv Genet 97:81–113

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Telenti A et al (2018) Deep learning of genomic variation and regulatory network data. Hum Mol Genet 27(R1):R63–R71

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Esteva A et al (2017) Dermatologist-level classification of skin cancer with deep neural networks. Nature 542(7639):115–118

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Gerstung M et al (2017) Precision oncology for acute myeloid leukemia using a knowledge bank approach. Nat Genet 49(3):332–340

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Gulshan V et al (2016) Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA 316(22):2402–2410

    Article  PubMed  Google Scholar 

  11. Cohen JD et al (2018) Detection and localization of surgically resectable cancers with a multi-analyte blood test. Science 359(6378):926–930

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Bray MA et al (2017) A dataset of images and morphological profiles of 30 000 small-molecule treatments using the Cell Painting assay. Gigascience 6(12):1–5

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Ma J et al (2018) Using deep learning to model the hierarchical structure and function of a cell. Nat Methods 15(4):290–298

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Ideker T et al (2001) Integrated genomic and proteomic analyses of a systematically perturbed metabolic network. Science 292(5518):929–934

    Article  CAS  PubMed  Google Scholar 

  15. Bohannon J (2017) The cyberscientist. Science 357(6346):18–21

    Article  CAS  PubMed  Google Scholar 

  16. King RD et al (2009) The automation of science. Science 324(5923):85–89

    Article  CAS  PubMed  Google Scholar 

  17. Sparkes A, Clare A (2012) AutoLabDB: a substantial open source database schema to support a high-throughput automated laboratory. Bioinformatics 28(10):1390–1397

    Article  CAS  PubMed  Google Scholar 

  18. Butler KT et al (2018) Machine learning for molecular and materials science. Nature 559(7715):547–555

    Article  CAS  PubMed  Google Scholar 

  19. Sanchez-Lengeling B, Aspuru-Guzik A (2018) Inverse molecular design using machine learning: generative models for matter engineering. Science 361(6400):360–365

    Article  CAS  PubMed  Google Scholar 

  20. Aage N et al (2017) Giga-voxel computational morphogenesis for structural design. Nature 550(7674):84–86

    Article  CAS  PubMed  Google Scholar 

  21. Segler MHS, Preuss M, Waller MP (2018) Planning chemical syntheses with deep neural networks and symbolic AI. Nature 555(7698):604–610

    Article  CAS  PubMed  Google Scholar 

  22. Ahneman DT et al (2018) Predicting reaction performance in C-N cross-coupling using machine learning. Science 360(6385):186–190

    Article  CAS  PubMed  Google Scholar 

  23. Radovic A et al (2018) Machine learning at the energy and intensity frontiers of particle physics. Nature 560(7716):41–48

    Article  CAS  PubMed  Google Scholar 

  24. Silver D et al (2017) Mastering the game of Go without human knowledge. Nature 550(7676):354–359

    Article  CAS  PubMed  Google Scholar 

  25. Madhukar NS et al (2018) A new big-data paradigm for target identification and drug discovery. BioRxiv. https://doi.org/10.1101/134973

  26. Chen B, Butte AJ (2016) Leveraging big data to transform target selection and drug discovery. Clin Pharmacol Ther 99(3):285–297

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Hu Y, Bajorath J (2017) Entering the ‘big data’ era in medicinal chemistry: molecular promiscuity analysis revisited. Future Sci OA 3(2):FSO179

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  28. Hernandez D (2018) How robots are making better drugs, faster. In: Wall Street Journal. Dow Jones & Company, New York, NY

    Google Scholar 

  29. Patient-centered drug manufacture (2017) Nat Biotechnol 35(6):485

    Article  CAS  Google Scholar 

  30. Schellekens H et al (2017) Making individualized drugs a reality. Nat Biotechnol 35(6):507–513

    Article  CAS  PubMed  Google Scholar 

  31. Lavertu A et al (2018) Pharmacogenomics and big genomic data: from lab to clinic and back again. Hum Mol Genet 27(R1):R72–R78

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Kalinin AA et al (2018) Deep learning in pharmacogenomics: from gene regulation to patient stratification. Pharmacogenomics 19(7):629–650

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Schork NJ (2015) Personalized medicine: time for one-person trials. Nature 520(7549):609–611

    Article  CAS  PubMed  Google Scholar 

  34. Serhani MA et al (2017) New algorithms for processing time-series big EEG data within mobile health monitoring systems. Comput Methods Programs Biomed 149:79–94

    Article  PubMed  Google Scholar 

  35. Marr B (2017) First FDA approval for clinical cloud-based deep learning in healthcare. In: Forbes. Forbes Publishing Company, New York City

    Google Scholar 

  36. Miotto R et al (2016) Deep patient: an unsupervised representation to predict the future of patients from the electronic health records. Sci Rep 6:26094

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Rossi G, Manfrin A, Lutolf MP (2018) Progress and potential in organoid research. Nat Rev Genet

    Google Scholar 

  38. Deng Y et al (2018) Massive single-cell RNA-seq analysis and imputation via deep learning. BioRxiv. https://t.co/EGBwlYFLLK

  39. Yauney G, Shah P (2018) Reinforcement learning with action-derived rewards for chemotherapy and clinical trial dosing regimen selection. In: Proceedings of machine learning research, vol 85

    Google Scholar 

  40. Biankin AV, Piantadosi S, Hollingsworth SJ (2015) Patient-centric trials for therapeutic development in precision oncology. Nature 526(7573):361–370

    Article  CAS  PubMed  Google Scholar 

  41. Kodack DP et al (2017) Primary patient-derived cancer cells and their potential for personalized cancer patient care. Cell Rep 21(11):3298–3309

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Gorshkov K et al (2018) Advancing precision medicine with personalized drug screening. Drug Discov Today

    Google Scholar 

  43. Miranda CC et al (2018) Towards multi-organoid systems for drug screening applications. Bioengineering (Basel) 5(3)

    Article  PubMed Central  Google Scholar 

  44. Scott IA et al (2018) Using EMR-enabled computerized decision support systems to reduce prescribing of potentially inappropriate medications: a narrative review. Ther Adv Drug Saf 9(9):559–573

    Article  PubMed  PubMed Central  Google Scholar 

  45. Dreyer KJ, Geis JR (2017) When machines think: radiology’s next frontier. Radiology 285(3):713–718

    Article  PubMed  Google Scholar 

  46. Nabi J (2018) How bioethics can shape artificial intelligence and machine learning. Hastings Cent Rep 48(5):10–13

    Article  PubMed  Google Scholar 

  47. Etheredge LM (2007) A rapid-learning health system. Health Aff (Millwood) 26(2):w107–w118

    Article  Google Scholar 

  48. Shrager J, Tenenbaum JM (2014) Rapid learning for precision oncology. Nat Rev Clin Oncol 11(2):109–118

    Article  PubMed  Google Scholar 

  49. Shah A et al (2016) Building a rapid learning health care system for oncology: why CancerLinQ collects identifiable health information to achieve its vision. J Clin Oncol 34(7):756–763

    Article  PubMed  Google Scholar 

  50. Hastie T, Tibshirani R, Friedman J (2009) The elements of statistical learning: data mining, inference, and prediction, 2nd edn. Springer, New York

    Book  Google Scholar 

  51. Schork NJ, Goetz LH (2017) Single-subject studies in translational nutrition research. Annu Rev Nutr 37:395–422

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Agarwala V et al (2018) Real-world evidence in support of precision medicine: clinico-genomic cancer data as a case study. Health Aff (Millwood) 37(5):765–772

    Article  Google Scholar 

  53. Williams MS et al (2018) Patient-centered precision health in a learning health care system: Geisinger’s genomic medicine experience. Health Aff (Millwood) 37(5):757–764

    Article  Google Scholar 

  54. Rajkomar A et al (2018) Scalable and accurate deep learning with electronic health records. NPJ Digit Med 1(18)

    Google Scholar 

  55. Ali M, Aittokallio T (2018) Machine learning and feature selection for drug response prediction in precision oncology applications. Biophys Rev

    Google Scholar 

  56. Mathe E et al (2018) The omics revolution continues: the maturation of high-throughput biological data sources. Yearb Med Inform 27(1):211–222

    Article  PubMed  PubMed Central  Google Scholar 

  57. Varghese J et al (2018) CDEGenerator: an online platform to learn from existing data models to build model registries. Clin Epidemiol 10:961–970

    Article  PubMed  PubMed Central  Google Scholar 

  58. Lerner I et al (2018) Revolution in health care: how will data science impact doctor-patient relationships? Front Public Health 6:99

    Article  PubMed  PubMed Central  Google Scholar 

  59. Savage N (2017) Machine learning: calculating disease. Nature 550(7676):S115–S117

    Article  CAS  PubMed  Google Scholar 

  60. Bycroft C et al (2018) The UK Biobank resource with deep phenotyping and genomic data. Nature 562:203–209

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. Sankar PL, Parker LS (2017) The Precision Medicine Initiative’s All of Us Research Program: an agenda for research on its ethical, legal, and social issues. Genet Med 19(7):743–750

    Article  PubMed  Google Scholar 

  62. Li C et al (2018) Application of induced pluripotent stem cell transplants: autologous or allogeneic? Life Sci

    Google Scholar 

  63. Graham C et al (2018) Allogeneic CAR-T cells: more than ease of access? Cells 7(10)

    Article  PubMed Central  Google Scholar 

  64. Tan R, Yang X, Shen Y (2017) Robot-aided electrospinning toward intelligent biomedical engineering. Robotics Biomim 4(1):17

    Article  PubMed  PubMed Central  Google Scholar 

  65. Osouli-Bostanabad K, Adibkia K (2018) Made-on-demand, complex and personalized 3D-printed drug products. Bioimpacts 8(2):77–79

    Article  PubMed  PubMed Central  Google Scholar 

  66. Schork NJ (2018) Randomized clinical trials and personalized medicine: a commentary on deaton and cartwright. Soc Sci Med 210:71–73

    Article  PubMed  PubMed Central  Google Scholar 

  67. Shamsuddin R et al (2018) Virtual patient model: an approach for generating synthetic healthcare time series data. In: IEEE international conference on healthcare informatics. IEEE Computer Society

    Google Scholar 

  68. Fisher AJ, Medaglia JD, Jeronimus BF (2018) Lack of group-to-individual generalizability is a threat to human subjects research. Proc Natl Acad Sci U S A 115(27):E6106–E6115

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  69. Drescher CW et al (2013) Longitudinal screening algorithm that incorporates change over time in CA125 levels identifies ovarian cancer earlier than a single-threshold rule. J Clin Oncol 31(3):387–392

    Article  PubMed  Google Scholar 

  70. Zhou N et al (2018) Concordance study between IBM Watson for Oncology and clinical practice for patients with cancer in China. Oncologist

    Google Scholar 

  71. Schmidt C (2017) M. D. Anderson breaks with IBM Watson, raising questions about artificial intelligence in oncology. J Natl Cancer Inst 109(5):4–5

    Article  Google Scholar 

  72. Abramoff MD et al (2018) Pivotal trial of an autonomous AI-based diagnostic system for detection of diabetic retinopathy in primary care offices. NPJ Digit Med 1(39)

    Google Scholar 

  73. Lazer D et al (2014) Big data. The parable of Google Flu: traps in big data analysis. Science 343(6176):1203–1205

    Article  CAS  PubMed  Google Scholar 

  74. Le Tourneau C et al (2015) Molecularly targeted therapy based on tumour molecular profiling versus conventional therapy for advanced cancer (SHIVA): a multicentre, open-label, proof-of-concept, randomised, controlled phase 2 trial. Lancet Oncol 16(13):1324–1334

    Article  PubMed  CAS  Google Scholar 

  75. Ioannidis JPA, Khoury MJ (2018) Evidence-based medicine and big genomic data. Hum Mol Genet 27(R1):R2–R7

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  76. AI diagnostics need attention (2018) Nature 555(7696):285

    Google Scholar 

  77. Frieden TR (2017) Evidence for health decision making—beyond randomized, controlled trials. N Engl J Med 377(5):465–475

    Article  PubMed  Google Scholar 

  78. Abernethy A, Khozin S (2017) Clinical drug trials may be coming to your doctor’s office. In: Wall Street Journal. Dow Jones & Company, New York, NY

    Google Scholar 

  79. Voosen P (2017) The AI detectives. Science 357(6346):22–27

    Article  CAS  PubMed  Google Scholar 

  80. Marwala T (2015) Causality, correlation and artificial intelligence for rational decision making. World Scientific, New Jersey

    Book  Google Scholar 

  81. Ciliberto C et al (2018) Quantum machine learning: a classical perspective. Proc Math Phys Eng Sci 474(2209):20170551

    Article  PubMed  PubMed Central  Google Scholar 

  82. Li RY et al (2018) Quantum annealing versus classical machine learning applied to a simplified computational biology problem. npj Quantum Inf 4

    Google Scholar 

  83. Vashistha R et al (2018) Futuristic biosensors for cardiac health care: an artificial intelligence approach. 3 Biotech 8(8):358

    Article  PubMed  PubMed Central  Google Scholar 

  84. Palsson B (2015) Systems biology: constraint-based reconstruction and analysis, 2nd edn. Cambridge University Press, Boston, MA

    Book  Google Scholar 

  85. Ideker T, Dutkowski J, Hood L (2011) Boosting signal-to-noise in complex biology: prior knowledge is power. Cell 144(6):860–863

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  86. Khera AV et al (2018) Genome-wide polygenic scores for common diseases identify individuals with risk equivalent to monogenic mutations. Nat Genet 50(9):1219–1224

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  87. Warren M (2018) The approach to predictive medicine that is taking genomics research by storm. Nature 562(7726):181–183

    Article  PubMed  Google Scholar 

  88. Schork AJ, Schork MA, Schork NJ (2018) Genetic risks and clinical rewards. Nat Genet 50(9):1210–1211

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  89. Patel CJ et al (2013) Whole genome sequencing in support of wellness and health maintenance. Genome Med 5(6):58

    Article  PubMed  PubMed Central  Google Scholar 

  90. Schork NJ (2013) Genetic parts to a preventive medicine whole. Genome Med 5(6):54

    Article  PubMed  PubMed Central  Google Scholar 

  91. Mapara SS, Patravale VB (2017) Medical capsule robots: a renaissance for diagnostics, drug delivery and surgical treatment. J Control Release 261:337–351

    Article  CAS  PubMed  Google Scholar 

  92. Topol EJ (2019) Deep medicine: how artificial intelligence can make healthcare human again. Basic Books, New York

    Google Scholar 

  93. David LA et al (2014) Host lifestyle affects human microbiota on daily timescales. Genome Biol 15(7):R89

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  94. Chen R et al (2012) Personal omics profiling reveals dynamic molecular and medical phenotypes. Cell 148(6):1293–1307

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  95. Magnuson V, Wang Y, Schork N (2016) Normalizing sleep quality disturbed by psychiatric polypharmacy: a single patient open trial (SPOT). F1000Research 5:132

    PubMed  PubMed Central  Google Scholar 

  96. Zeevi D et al (2015) Personalized nutrition by prediction of glycemic responses. Cell 163(5):1079–1094

    Article  CAS  PubMed  Google Scholar 

  97. Smarr L et al (2017) Tracking human gut microbiome changes resulting from a colonoscopy. Methods Inf Med 56(6):442–447

    Article  PubMed  Google Scholar 

  98. Trammell SA et al (2016) Nicotinamide riboside is uniquely and orally bioavailable in mice and humans. Nat Commun 7:12948

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  99. Forsdyke DR (2015) Summertime dosage-dependent hypersensitivity to an angiotensin II receptor blocker. BMC Res Notes 8:227

    Article  PubMed  PubMed Central  Google Scholar 

  100. O’Rawe JA et al (2013) Integrating precision medicine in the study and clinical treatment of a severely mentally ill person. PeerJ 1:e177

    Article  PubMed  PubMed Central  Google Scholar 

  101. Li W et al (2016) Extensive graft-derived dopaminergic innervation is maintained 24 years after transplantation in the degenerating parkinsonian brain. Proc Natl Acad Sci U S A 113(23):6544–6549

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  102. Bloss CS et al (2015) A genome sequencing program for novel undiagnosed diseases. Genet Med 17(12):995–1001

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  103. Piening BD et al (2018) Integrative personal omics profiles during periods of weight gain and loss. Cell Syst 6(2):157–170 e8

    Google Scholar 

  104. Zalusky R, Herbert V (1961) Megaloblastic anemia in scurvy with response to 50 microgm. of folic acid daily. N Engl J Med 265:1033–1038

    Article  CAS  PubMed  Google Scholar 

  105. Herbert V (1962) Experimental nutritional folate deficiency in man. Trans Assoc Am Physicians 75:307–320

    CAS  PubMed  Google Scholar 

  106. Golding PH (2014) Severe experimental folate deficiency in a human subject—a longitudinal study of biochemical and haematological responses as megaloblastic anaemia develops. Springerplus 3:442

    Article  PubMed  PubMed Central  CAS  Google Scholar 

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Authors and Affiliations

  1. Department of Quantitative Medicine, The Translational Genomics Research Institute (TGen), Phoenix, AZ, USA

    Nicholas J. Schork

  2. The City of Hope/TGen IMPACT Center, Duarte, CA, USA

    Nicholas J. Schork

  3. The University of California San Diego, La Jolla, CA, USA

    Nicholas J. Schork

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Editors and Affiliations

  1. Molecular Medicine Division, Translational Genomics Research Institute, Phoenix, AZ, USA

    Daniel D. Von Hoff

  2. Molecular Medicine Division, Translational Genomics Research Institute, Phoenix, AZ, USA

    Haiyong Han

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Schork, N.J. (2019). Artificial Intelligence and Personalized Medicine. In: Von Hoff, D., Han, H. (eds) Precision Medicine in Cancer Therapy . Cancer Treatment and Research, vol 178. Springer, Cham. https://doi.org/10.1007/978-3-030-16391-4_11

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  • DOI: https://doi.org/10.1007/978-3-030-16391-4_11

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