[language] Another influential book

[H.M. Hubey]

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Another highly influential book in the forefront. It is a part of a series.

------------------------------Series Foreword------------------
Like bioinformatics, the field of machine learning is interdisciplinary.
The goal of building
computer systems that can adapt to environments and learn from
experience has attracted
researchers from from many fields, including computer science,
engineering, mathematics,
physics, neuroscience, and cognitive science. Out of this research has
come a variety of
techniques that have the potential to transform many scientific and
industrial fields. Several
research communities have converged on a common set of issues
surrounding supervised,
unsupervised, and reinforcement learning problems...
                                                        Thomas Dietterich

------------------------------Preface
------------------------------------------

In all areas of biological and medical research, the role of the
computer has been dramatically
enhanced in the last five to ten year period.... The main driving forcee
behind the changes has
been the advent of new, efficient experimental techniques, primarily DNA
sequencing, that
have led to an exponential growth of linear descriptions of protein, DNA
and RNA molecules.
.....As a result, computational support in experimental design,
processing of results and
interpretation of results has become essential....

The large amounts of data create a critical need for theoretical,
algorithmic, and software
advances in storing, retrieving, networking, processing, analyzing,
navigating, and
visualizing biological information. In turn, biological systems have
inspired computer science
advances with new concepts, including genetic algorithms, artificial
neural networks, computer
viruses, and synthetic immune systems, DNA computing, artificial life,
and hybrid VLSI-DNA
gene chips. This cross-fertilization has enriched both fields and will
continue to do so in the
coming decades. In fact, all the boundaries between carbon-based and
silicon-based
information processing systems, whether conceptual or material have
begun to shrink.
...
Bioinformatics has emerged as a strategic discipline at the frontier
between biology and
computer science, impacting medicine, biotechnology, and society in many
ways.
...
....conventional computer science algorithms...are increasingly unable
to address many of
the most interesting sequence analysis problems. This is due to the
inherent complexity of
biological systems, brought about by evolutionary tinkering, and our
lack of comprehensive
theory of life's organization at the molecular level. Machine-learning
approaches (e.g. neural
networks, hidden Markov models, vector support machines, belief
networks), on the other
hand, are ideally suited for domains characterized by the presence of
large amounts of
data, "noisy" patterns, and the absence of general theories. The
fundamental idea behind
these approaches is to learn the theory automatically from the data,
through a process of
inference, model fitting, or learning from examples. They form a viable
complementary
approach to conventional methods. The aim of this book is to present a
broad overview
of bioinformatics from a machine-learning perspective.
...
An often-met criticism of machine-learning techniques is that they are
"black-box" approaches"
one cannot always pin down exactly how a complex neural network, or
hidden Markov
model, reaches a particular answer. We have tried to address such
legitimate concerns both
within the general probabilistic framework and from a practical
standpoint. It is important to
realize, however, that many other techniques in contemporary molecular
biology are used on
a purely empirical basis. The polymerase chain reaction, for example,
for all its usefulness and
sensitivity, is still somewhat of a black-box technique. Many of its
adjustable parameters are
chosen on a trial-and-error basis. .... Ultimately the proof is in the
pudding. We have striven
to show that machine-learning methods yield good puddings and are being
elegant at the
same time.

....We have tried to provide a succinct description of the main
biological concepts and problems
for the readers with a stronger background in mathematics, statistics
and computer science.
Likewise, the book is tailored to biologists and biochemists who will
often know more about
the biological problems than the text explains, but need some help to
understand the new
data-driven algorithms, in the context of biological data. .... The
technical prerequisites for
the book are basic calculus, algebra, and discrete probabiltiy theory,
at the level of an
undergraduate course.

                                               Pierre Baldi
                                                Soren Brunak
                                                MIT Press

-------------------------------------------------------------------------------------
Anyone with the prerequisites should/could start reading about this
exciting field
and maybe even apply some of the ideas to linguistics.

--
M. Hubey
-o-o-o-o-o-o-o-o-o-o-o-o-o-o-o-o-o-o-o-o-o-o-o-o-o-o-o-o-o-o-o-o-o-o
The only difference between humans and machines is that humans
can be created by unskilled labor. Arthur C. Clarke

/\/\/\/\//\/\/\/\/\/\/ http://www.csam.montclair.edu/~hubey



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