MACHINE LEARNING JOURNAL
Special Issue
Methods in Functional Genomics
http://www.genomethods.org/mlj/
Guest editors:
Paola Sebastiani (University of Massachusetts, Amherst)
Marco Ramoni (Harvard Medical School)
Isaac Kohane (Harvard Medical School)
In June 2000, leaders of the Human Genome Project, Craig Venter of
Celera Genomics, and U.S. President Clinton announced the completion
of a ``working draft" DNA sequence of the human genome: the genetic
blueprint for a human being. The legacy of that a announcement is the
challenge to annotate this map, by understanding the functions of
genes and their interplay with proteins and the environment to create
complex, dynamic living systems. This understanding is the goal of
functional genomics.
Recent technological advances enable biomedical investigators to
observe the genome of entire organisms in action by simultaneously
measuring the level of activation of thousands of genes under the same
experimental conditions. This technology, known as microarrays,
provides today unprecedented discovery opportunities and it is
reshaping biomedical sciences. Parallel to these technological
advances has been the development of machine learning methods able
to integrate and understand the data generated by this new kind of
experiments. However, most of this research has been conducted outside
the traditional machine learning research community. The aim of this
special issue is to bridge this divide by inviting researches to
communicate their methodological advances in automated learning from
functional genomics to the core machine learning community. The
special issue seeks contributions of significant methodological
content and high potential impact on the functional genomics research
community.
Topics of particular interest include, but are not limited to:
- - Hybridization detection, signal amplification and noise control in
microarray experiments.
- - Differential analysis and classification of gene expression data.
- - Clustering and other unsupervised approaches to class discovery in
functional genomics.
- - Temporal profiling and analysis of dynamic genomic systems.
- - Dependency discovery and reverse engineering of genetic networks.
- - Validation methods to assess reliability and reproducibility of
experiments.
Articles accepted for the special issue will be permanently posted on
the WWW and will constitute the core of a free portal to AI and
machine learning resources in bioinformatics (www.genomethods.org).
The production of the Special Issue is expected to proceed according
to the following schedule:
May 15, 2002 Submission of a preliminary abstract.
June 15, 2002 Full manuscripts submission.
Sept 1, 2002 Notification of acceptance.
December 1, 2002 Final revisions due.
Spring 2003 Special Issue publication.
Submission instructions:
1. Authors who intend to contribute to the special issue should send a
tentative title and abstract to Paola Sebastiani at the address below
before May 15, 2002.
2. Manuscripts should conform to the standard formatting instructions
of Machine Learning Journal, available from
http://www.cs.ualberta.ca/~holte/mlj/initialsubmission.pdf.
3. All submissions should be made electronically, as a postscript or
pdf attachment to: jml@wkap.com. Clearly state, in the body of your
email, that your submission is for the special issue "Methods in
Functional Genomics".
Authors can also send a copy of the manuscript at the address below.
Address general inquiries to:
Paola Sebastiani
Department of Mathematics and Statistics
University of Massachusetts at Amherst
1436 Lederle Graduate Research Tower
Amherst, MA 01002
Phone: (413) 545 0622
Fax: (413) 545 1801
Email: sebas@math.umass.edu
------------------------------------------------------------------
Paola Sebastiani
Assistant Professor
Department of Mathematics and Statistics
University of Massachusetts,
Amherst, MA, 01003
Telephone: (413) 545-0622
Fax: (413) 545-1801
email: sebas@math.umass.edu
url:http://www.math.umass.edu/~sebas
This archive was generated by hypermail 2b29 : Wed Mar 06 2002 - 20:33:04 PST