Abstract
The analysis of biological data asks for a delicate balance of content-specific and procedural knowledge; this is why it is virtually impossible to apply standard mathematical and statistical recipes to systems biology.
The separation of the important part of information from singular (and largely irrelevant) details implies a continuous interchange between biological and statistical knowledge. The generalization ability of the models must be the principal focus of system’s parameter estimation, while the multi-scale character of biological regulation orients the modeling style toward data-driven strategies based on the correlation structure of the analyzed systems.
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References
Transtrum MK et al (2015) Perspective: sloppiness and emergent theories in physics, biology and beyond. J Chem Phys 143:01091
Kullback S, Leibler RA (1951) On information and sufficiency. Ann Math Stat 22(1):79–86
Srivastava N et al (2014) Dropout: a simple way to prevent neural networks from overfitting. J Mach Learn Res 15(1):1929–1958
Tropsha A (2010) Best practices for QSAR model development, validation, and exploitation. Mol Inform 29(6–7):476–488
Pearson K (1901) On lines and planes of closest fit to systems of points in space. Lond Edinb Dubl Phil Mag J Sci 2(11):559–572
Giuliani A (2017) The application of principal component analysis to drug discovery and biomedical data. Drug Discov Today 22(7):1069–1076
Soofi E (1994) Capturing the intangible concept of information. J Am Stat Assoc 89(428):1243–1254
Pascual M, Levin SA (1999) From individuals to population densities: searching for the intermediate scale of nontrivial determinism. Ecology 80(7):2225–2236
Broomhead DS, King GP (1986) Extracting qualitative dynamics from experimental data. Physica D 20(2–3):217–236
Benigni R, Giuliani A (1994) Quantitative modeling and biology: the multivariate approach. Am J Phys Regul Integr Comp Phys 266(5):R1697–R1704
Marwan N et al (2007) Recurrence plots for the analysis of complex systems. Phys Rep 438(5):237–329
Kruskal JB (1964) Multidimensional scaling by optimizing goodness of fit to a nonmetric hypothesis. Psychometrika 29(1):1–27
Anderberg MR (2014) Cluster analysis for applications: probability and mathematical statistics: a series of monographs and textbooks, vol 19. Academic, Cambridge
Simonelli V et al (2016) Crosstalk between mismatch repair and base excision repair in human gastric cancer. Oncotarget 5. 10.18632/oncotarget.10185
Weaver W (1948) Science and complexity. Am Sci 36:536–549
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Giuliani, A. (2018). The Search for System’s Parameters. In: Bizzarri, M. (eds) Systems Biology. Methods in Molecular Biology, vol 1702. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-7456-6_5
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DOI: https://doi.org/10.1007/978-1-4939-7456-6_5
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