Abstract
This chapter is intended to pave the way towards the increasing automation of data and even knowledge analysis. The driver for this trend is the enormous increase in the quantity of information that should be assimilated. Topics that need to be considered in this context include the representation and the structure of knowledge. The problem of bacterial identification is presented as an example. Going beyond the automated analysis of “big data” is the analysis of text—text mining, the goal of which is the automated inference of semantic information. In parallel to that, it is now possible to at least partially automate the production of data, using laboratory robots that can not only manipulate reagents but also, to some extent, design experiments.
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Notes
- 1.
A particularly glaring example of disrespect toward this principle is to be found in the current fashion among museum curators to ceaselessly rearrange their collections in order to demonstrate some preconceived idea or another, whereas, ideally, the exhibits should be displayed in an unstructured manner, in order to allow the thoughtful visitor to draw his or her own conclusions from the raw evidence. Only in that way can new knowledge (conditional information) be generated through the perception of new, hitherto unperceived, relationships.
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Pointed out by Frauenfelder (1984).
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Each different model—such as RiboWeb, EcoCyc—is typically called an “ontology”; hence, we have the Gene Ontology, the Transparent Access to Multiple Bioinformatics Information Sources (TAMBIS) Ontology (Baker et al. 1999), and so forth. If ontology is given the restricted meaning of the study of classes of objects, then an “ontology” like TAMBIS can be considered to be the product of ontological inquiry.
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Beall (2014).
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King et al. (2009).
References
Baker PG, Goble CA, Bechhofer S et al (1999) An ontology for bioinformatics applications. Bioinformatics 15:510–520
Beall J (2014) Scholarly open-access publishing and the problem of predatory publishers. J Biol Phys Chem 14:22–24
Coenye T, Vandamme P (2004) Use of the genomic signature in bacterial classification and identification. Syst Appl Microbiol 27:175–185
Frauenfelder H (1984) From atoms to biomolecules. Helv Phys Acta 57:165–187
Good IJ (1962) Botryological speculations. In: Good IJ (ed) The scientist speculates. Heinemann, London, pp 120–132
Hanage WP, Fraser C, Spratt BG (2006) Sequences, sequence clusters and bacterial species. Phil Trans R Soc B 361:1917–1927
King RD et al (2009) The automation of science. Science 324:85–89
Sommerhoff G (1950) Analytical biology. Oxford University Press, London
Trüper HG (1999) How to name a prokaryote? FEMS Microbiol Rev 23:231–249
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Ramsden, J. (2015). The Organization of Knowledge. In: Bioinformatics. Computational Biology, vol 21. Springer, London. https://doi.org/10.1007/978-1-4471-6702-0_22
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DOI: https://doi.org/10.1007/978-1-4471-6702-0_22
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