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Books > Science & Mathematics > Biology, life sciences > Molecular biology
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Data Mining Techniques for the Life Sciences (Paperback, Softcover reprint of the original 1st ed. 2010)
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Data Mining Techniques for the Life Sciences (Paperback, Softcover reprint of the original 1st ed. 2010)
Series: Methods in Molecular Biology, 609
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Most life science researchers will agree that biology is not a
truly theoretical branch of science. The hype around computational
biology and bioinformatics beginning in the nineties of the 20th
century was to be short lived (1, 2). When almost no value of
practical importance such as the optimal dose of a drug or the
three-dimensional structure of an orphan protein can be computed
from fundamental principles, it is still more straightforward to
determine them experimentally. Thus, experiments and
observationsdogeneratetheoverwhelmingpartofinsightsintobiologyandmedicine.
The extrapolation depth and the prediction power of the theoretical
argument in life sciences still have a long way to go. Yet, two
trends have qualitatively changed the way how biological research
is done today. The number of researchers has dramatically grown and
they, armed with the same protocols, have produced lots of
similarly structured data. Finally, high-throu- put technologies
such as DNA sequencing or array-based expression profiling have
been around for just a decade. Nevertheless, with their high level
of uniform data generation, they reach the threshold of totally
describing a living organism at the biomolecular level for the
first time in human history. Whereas getting exact data about
living systems and the sophistication of experimental procedures
have primarily absorbed the minds of researchers previously, the
weight increasingly shifts to the problem of interpreting
accumulated data in terms of biological function and bio- lecular
mechanisms.
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