CFPs: Information theory in biology, Jan 99, Hawaii

[David L Dowe]

   Dear All,

      Apologies for cross-postings.

In short,

if

you're interested in
MML or MDL or Akaike's Information Criterion or information theory
and you're interested in biology,
and you'd like to go to Hawaii in January 1999,
and you can get a paper ready by July 1998,

then

store this mail away and bookmark the site
http://www.cs.monash.edu.au/~dld/PSB99/PSB99.Info.CFPs.html
and read on.

===================================================================

This is the Call For Papers for the 4th Pacific Symposium on BioComputing
(PSB99, 1999) conference track on "Information-theoretic approaches
to biology".

PSB-99 will be held from 4-9 January, 1999, in Mauni Lani on the
Big Island of Hawaii.

Track Organisers: David L. Dowe (dld at cs.monash.edu.au) and Klaus Prank.

WWW site:  http://www.cs.monash.edu.au/~dld/PSB99/PSB99.Info.CFPs.html .

Specific technical area to be covered by this track:

Approaches to biological problems using notions of information or complexity,
including methods such as Algorithmic Probability, Minimum Message Length and
Minimum Description Length. Two possible applications are (e.g.) protein
folding and biological information processing.
Kolmogorov (1965) and Chaitin (1966) studied the notions of complexity and
randomness, with Solomonoff (1964), Wallace (1968) and Rissanen (1978) applying
these to problems of statistical and inferential learning (and ``data mining'')
and to prediction.  The methods of Solomonoff, Wallace and Rissanen have
respectively come to be known as Algorithmic Probability (ALP), Minimum Message
Length (MML) and Minimum Description Length (MDL).  All of these methods relate
to information theory, and can also be thought of in terms of Shannon's
information theory, and can also be thought of in terms of Boltzmann's
thermo-dynamic entropy.

An MDL/MML perspective has been suggested by a number of authors in the context
of approximating unknown functions with some parametric approximation scheme
(such as a neural network). The designated measure to optimize under this
scheme combines an estimate of the cost of misfit with an estimate of the cost
of describing the parametric approximation (Akaike 1973, Rissanen 1978,
Barron and Barron 1988, Wallace and Boulton, 1968).

This track invites all original papers of a biological nature which use
notions of information and/or information-theoretic complexity, with no strong
preference as to what specific nature.  Such work has been done in problems of,
e.g., protein folding and DNA string alignment.  As we shortly describe in some
detail, such work has also been done in the analysis of temporal dynamics in
biology such as neural spike trains and endocrine (hormonal) time series
analysis using the MDL principle in the context of neural networks and
context-free grammar complexity.

To elaborate on one of the relevant topics above, in the last three years
or so, there has been a major focus on the aspect of timing in biological
information processing ranging from fields such as neuroscience to
endocrinology. The latest work on information processing at the single-cell
level using computational as well as experimental approaches reveals previously
unimagined complexity and dynamism. Timing in biological information processing
on the single-cell level as well as on the systems level has been studied by
signal-processing and information-theoretic approaches in particular in the
field of neuroscience (see for an overview: Rieke et al. 1996). Using such
approaches to the understanding of temporal complexity in biological
information transfer, the maximum information rates and the precision of spike
timing to the understanding of temporal complexity in biological information
transfer, the maximum information rates and the precision of spike timing could
be revealed by computational methods (Mainen and Sejnowski, 1995; Gabbiani and
Koch 1996; Gabbiani et al., 1996).

The examples given above are examples of some possible biological application
domains.  We invite and solicit papers in all areas of (computational) biology
which make use of ALP, MDL, MML and/or other notions of information and
information-theoretic complexity.

In problems of prediction, as well as using "yes"/"no" predictions, we would
encourage the authors to consider also using probabilistic prediction, where
the score assigned to a probabilistic prediction is given according to the
negative logarithm of the stated probability of the event.




Further comments re PSB-99 :
----------------------------
 PSB99 will publish accepted full papers in an archival Proceedings. All
 contributed papers will be rigorously peer-reviewed by at least three
 referees.  Each accepted full paper will be allocated up to 12 pages in the
 conference Proceedings. The best papers will be selected for a 30-minute
 oral presentation to the full assembled conference. Accepted poster
 abstracts will be distributed at the conference separately from the
 archival Proceedings. To be eligible for proceedings publication, each full
 paper must be accompanied by a cover letter stating that it contains
 original unpublished results not currently under consideration elsewhere.
 See http://www.cgl.ucsf.edu/psb/cfp.html for more information.

 IMPORTANT DATES:

 Full paper submissions due:			July   13, 1998
 Poster abstracts due:				August 22, 1998
 Notification of paper acceptance:		September 22, 1998
 Camera-ready copy due:				October 1, 1998
 Conference:					January 4 - 9, 1999



More information about the  "Information-theoretic approaches to biology"
track, including a sample list of relevant papers is available on
the WWW at  http://www.cs.monash.edu.au/~dld/PSB99/PSB99.Info.CFPs.html .

More information about PSB99 is available from
http://www.cgl.ucsf.edu/psb/cfp.html

For further information,
e-mail    Dr. David Dowe, dld at cs.monash.edu.au 
or e-mail Dr. Klaus Prank, ndxdpran at rrzn-serv.de .



This page was put together by
Dr. David Dowe,
School of Computer Science and Softw. Eng.,
Monash University, Clayton, Vic. 3168, Australia
e-mail: dld at cs.monash.edu.au
Fax: +61 3 9905-5146
http://www.csse.monash.edu.au/~dld/

and

Dr. Klaus Prank,
Abteilung Klinische Endokrinologie
Medizinische Hochschule Hannover
Carl-Neuberg-Str. 1
D-30623 Hannover
Germany
e-mail: ndxdpran at rrzn-serv.de
Tel.: +49 (511) 532-3827
Fax.: +49 (511) 532-3825
http://sun1.rrzn-user.uni-hannover.de/~ndxdpran/