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This volume details several important databases and data mining
tools. Data Mining Techniques for the Life Sciences, Second Edition
guides readers through archives of macromolecular three-dimensional
structures, databases of protein-protein interactions,
thermodynamics information on protein and mutant stability,
"Kbdock" protein domain structure database, PDB_REDO databank,
erroneous sequences, substitution matrices, tools to align RNA
sequences, interesting procedures for kinase family/subfamily
classifications, new tools to predict protein crystallizability,
metabolomics data, drug-target interaction predictions, and a
recipe for protein-sequence-based function prediction and its
implementation in the latest version of the ANNOTATOR software
suite. Written in the highly successful Methods in Molecular
Biology series format, chapters include introductions to their
respective topics, lists of the necessary materials and reagents,
step-by-step, readily reproducible laboratory protocols, and tips
on troubleshooting and avoiding known pitfalls. Authoritative and
cutting-edge, Data Mining Techniques for the Life Sciences, Second
Edition aims to ensure successful results in the further study of
this vital field.
This third edition details new and updated methods and protocols on
important databases and data mining tools. Chapters guides readers
through archives of macromolecular sequences and three-dimensional
structures, databases of protein-protein interactions, methods for
prediction conformational disorder, mutant thermodynamic stability,
aggregation, and drug response. Quality of structural data and
their release, soft mechanics applications in biology, and protein
flexibility are considered, too, together with pan-genome analyses,
rational drug combination screening and Omics Deep Mining. Written
in the format of the highly successful Methods in Molecular Biology
series, each chapter includes an introduction to the topic, lists
necessary materials, includes step-by-step, readily reproducible
protocols. Authoritative and cutting-edge, Data Mining Techniques
for the Life Sciences, Third Edition aims to be a practical guide
to researches to help further their study in this field.
This third edition details new and updated methods and protocols on
important databases and data mining tools. Chapters guides readers
through archives of macromolecular sequences and three-dimensional
structures, databases of protein-protein interactions, methods for
prediction conformational disorder, mutant thermodynamic stability,
aggregation, and drug response. Quality of structural data and
their release, soft mechanics applications in biology, and protein
flexibility are considered, too, together with pan-genome analyses,
rational drug combination screening and Omics Deep Mining. Written
in the format of the highly successful Methods in Molecular Biology
series, each chapter includes an introduction to the topic, lists
necessary materials, includes step-by-step, readily reproducible
protocols. Â Authoritative and cutting-edge, Data Mining
Techniques for the Life Sciences, Third Edition aims to be a
practical guide to researches to help further their study in this
field.
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.
This volume details several important databases and data mining
tools. Data Mining Techniques for the Life Sciences, Second Edition
guides readers through archives of macromolecular three-dimensional
structures, databases of protein-protein interactions,
thermodynamics information on protein and mutant stability,
"Kbdock" protein domain structure database, PDB_REDO databank,
erroneous sequences, substitution matrices, tools to align RNA
sequences, interesting procedures for kinase family/subfamily
classifications, new tools to predict protein crystallizability,
metabolomics data, drug-target interaction predictions, and a
recipe for protein-sequence-based function prediction and its
implementation in the latest version of the ANNOTATOR software
suite. Written in the highly successful Methods in Molecular
Biology series format, chapters include introductions to their
respective topics, lists of the necessary materials and reagents,
step-by-step, readily reproducible laboratory protocols, and tips
on troubleshooting and avoiding known pitfalls. Authoritative and
cutting-edge, Data Mining Techniques for the Life Sciences, Second
Edition aims to ensure successful results in the further study of
this vital field.
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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