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Big Data, Personalized Medicine and Network Pharmacology: Beyond the Current Paradigms

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Approaching Complex Diseases

Part of the book series: Human Perspectives in Health Sciences and Technology ((HPHST,volume 2))

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Abstract

The present reproducibility crisis of biological sciences stimulated a vibrant debate about the epistemological foundations of biomedicine. The wide recognition that the crisis stemmed from an acritical (and sometimes dull) application of statistical methods to the experimental results is the basis for re- examine the quantitative approaches to biology. Here we give a proof-of-concept of how the prophetic statements made by Weaver (one of the fathers of mathematical information theory) are coming true in our days, while ‘purely brute-force’ remedies, as Big Data approaches, do not give fully satisfactory remedies to the crisis.

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References

  1. Ioannidis, J.P. 2005. Why most published research findings are false. PLoS Medicine 2 (8): e1242.

    Article  Google Scholar 

  2. Nuzzo, R. 2014. Scientific method: Statistical errors. Nature 506 (7487): 150.

    Article  CAS  PubMed  Google Scholar 

  3. Young, S., and A. Kerr. 2011. Deming, data and observational studies a process out of control and needing fixing. Significance 8 (3): 116–120.

    Article  Google Scholar 

  4. Voosen, P. 2015, March 6. Amid a sea of false findings, the NIH tries reform. The Chronicle of Higher education

    Google Scholar 

  5. Munafò, Marcus R., et al. 2017. A manifesto for reproducible science. Nature Human Behaviour 1: 1–0021.

    Article  Google Scholar 

  6. Kraemer, H.C., and D.J. Kupfer. 2006. Size of treatment effects and their importance to clinical research and practice. Biological Psychiatry 59 (11): 990–996.

    Article  PubMed  Google Scholar 

  7. Richardson, J.T. 1996. Measures of effect size. Behavior Research Methods, Instruments, & Computers 28 (1): 12–22.

    Article  Google Scholar 

  8. Transtrum, M.K., B.B. Machta, K.S. Brown, B.C. Daniels, C.R. Myers, and J.P. Sethna. 2015. Perspective: Sloppiness and emergent theories in physics, biology, and beyond. The Journal of Chemical Physics 143 (1): 07B201_1.

    Article  Google Scholar 

  9. Agresti, A., and C.A. Franklin. 2007. Statistics: The art and science of learning from data. Upper Saddle River: Pearson Prentice Hall.

    Google Scholar 

  10. Pascual, M., and S.A. Levin. 1999. From individuals to population densities: Searching for the intermediate scale of nontrivial determinism. Ecology 80 (7): 2225–2236.

    Article  Google Scholar 

  11. Härdle, W., and L. Simar. 2007. Canonical correlation analysis. In Applied multivariate statistical analysis, 321–330. Berlin/Heidelberg: Springer.

    Google Scholar 

  12. Heagerty, P.J., and Y. Zheng. 2005. Survival model predictive accuracy and ROC curves. Biometrics 61 (1): 92–105.

    Article  PubMed  Google Scholar 

  13. Giuliani, A. 2017. The application of principal component analysis to drug discovery and biomedical data. Drug Discovery Today 22 (7): 1069–1076.

    Article  CAS  PubMed  Google Scholar 

  14. Weaver, W. 1948. Science and complexity. American Scientist 36: 536–549.

    CAS  PubMed  Google Scholar 

  15. Laughlin, R.B., D. Pines, J. Schmalian, B.P. Stojković, and P. Wolynes. 2000. The middle way. Proceedings of the National Academy of Sciences 97 (1): 32–37.

    Article  CAS  Google Scholar 

  16. Turing, A. M. 2006. Biological sequences and the exact string-matching problem. In Introduction to computational biology. Springer

    Google Scholar 

  17. Todde, V., and A. Giuliani. 2018. Big data. A briefing. Annali dell’Istituto Superiore di Sanità 54 (3): 174–175.

    PubMed  Google Scholar 

  18. Anderson, C. 2008. The end of theory: The data deluge makes the scientific method obsolete. Wired Magazine 16 (7): 16–07.

    Google Scholar 

  19. Calude, C.S., and G. Longo. 2017. The deluge of spurious correlations in big data. Foundations of Science 22 (3): 595–612.

    Article  Google Scholar 

  20. Gorban, A.N., and I.Y. Tyukin. 2018. Blessing of dimensionality: Mathematical foundations of the statistical physics of data. Philosophical Transactions of the Royal Society A – Mathematical Physical and Engineering Sciences 376 (2118): 20170237.

    Article  Google Scholar 

  21. Nicosia, V., M. De Domenico, and V. Latora. 2014. Characteristic exponents of complex networks. EPL (Europhysics Letters) 106 (5): 58005.

    Article  Google Scholar 

  22. Di Paola, L., M. De Ruvo, P. Paci, D. Santoni, and A. Giuliani. 2012. Protein contact networks: An emerging paradigm in chemistry. Chemical Reviews 113 (3): 1598–1613.

    Article  PubMed  Google Scholar 

  23. Hauser, T.U., V.G. Fiore, M. Moutoussis, and R.J. Dolan. 2016. Computational psychiatry of ADHD: Neural gain impairments across many levels of analysis. Trends in Neurosciences 39 (2): 63–73.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Tellegen, B. 1952. A general network theorem with application. Phillips Research Reports 7: 259–269.

    Google Scholar 

  25. Mickulecki, D. 2001. Network thermodynamics and complexity: A transition to relational systems theory. Computers & Chemistry 25: 369–391.

    Article  Google Scholar 

  26. Csermely, P., et al. 2013. Structure and dynamics of molecular networks: A novel paradigm of drug discovery: A comprehensive review. Pharmacology & Therapeutics 138: 333–408.

    Article  CAS  Google Scholar 

  27. Kohestani, H., and A. Giuliani. 2016. Organization principles of biological networks: An explorative study. Biosystems 141: 31–39.

    Article  CAS  PubMed  Google Scholar 

  28. Hopkins, A.L. 2008. Network pharmacology: The next paradigm in drug discovery. Nature Chemical Biology 4 (11): 682–690.

    Article  CAS  PubMed  Google Scholar 

  29. Ligeti, B., et al. 2015. A network-based target overlap score for characterizing drug combinations: High correlation with cancer clinical trial results. PLoS One 10 (6): e0129267.

    Article  PubMed  PubMed Central  Google Scholar 

  30. Csermely, P., et al. 2005. The efficiency of multi-target drugs: The network approach might help drug design. Trends in Pharmacological Sciences 26: 178–182.

    Article  CAS  PubMed  Google Scholar 

  31. Overington, J.P., et al. 2006. How many drug targets are there? Nature Reviews. Drug Discovery 5 (12): 993–996.

    Article  CAS  PubMed  Google Scholar 

  32. Huang, S. 2009. Reprogramming cell fates: Reconciling rarity with robustness. BioEssays 31 (5): 546–560.

    Article  CAS  PubMed  Google Scholar 

  33. Pagani, M., et al. 2016. Predicting the transition from normal aging to Alzheimer’s disease: A statistical mechanistic evaluation of FDG-PET data. NeuroImage 141: 282–290.

    Article  PubMed  Google Scholar 

  34. Prasad, V. 2016. Perspective: The precision-oncology illusion. Nature 537 (7619): S63.

    Article  CAS  PubMed  Google Scholar 

  35. Abrahams, E., and S.L. Eck. 2016. Molecular medicine: Precision oncology is not an illusion. Nature 539 (7629): 357.

    Article  CAS  PubMed  Google Scholar 

  36. Goh, W.W.B., and L. Wong. 2018. Dealing with confounders in omics analysis. Trends in Biotechnology 36 (5): 488–498.

    Article  CAS  PubMed  Google Scholar 

  37. Penny, K.I. 1996. Appropriate critical values when testing for a single multivariate outlier by using the Mahalanobis distance. Applied Statistics 45: 73–81.

    Article  Google Scholar 

  38. De Sanctis, R., A. Viganò, A. Giuliani, A. Gronchi, A. De Paoli, P. Navarria, V. Quagliuolo, A. Santoro, and A. Colosimo. 2018. Unsupervised versus supervised identification of prognostic factors in patients with localized retroperitoneal sarcoma (RPS): a data clustering and the Mahalanobis distance approach. Biomed Research International.

    Google Scholar 

  39. Schwartz, David N. 2017. The last man who knew everything: The Life and times of Enrico Fermi, father of the nuclear age. New York: Hachette.

    Google Scholar 

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Author information

Authors and Affiliations

  1. Istituto Superiore di Sanità, Roma, Italy

    Alessandro Giuliani & Virginia Todde

Authors
  1. Alessandro Giuliani
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  2. Virginia Todde
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Corresponding author

Correspondence to Alessandro Giuliani .

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Editors and Affiliations

  1. Department of Experimental Medicine, Sapienza University of Rome, Rome, Italy

    Mariano Bizzarri

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Giuliani, A., Todde, V. (2020). Big Data, Personalized Medicine and Network Pharmacology: Beyond the Current Paradigms. In: Bizzarri, M. (eds) Approaching Complex Diseases. Human Perspectives in Health Sciences and Technology, vol 2. Springer, Cham. https://doi.org/10.1007/978-3-030-32857-3_5

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