Call for contributions – New Problems and Methods in Computational Biology [NIPS 2010 MLCB workshop]

Call for contributions

New Problems and Methods in Computational Biology

A workshop at the Twenty-Third Annual Conference on
Neural Information Processing Systems (NIPS 2010)
Whistler, BC, Canada, December 10 or 11, 2010.

Deadline for submission of extended abstracts: Oct 25, 2010,


The field of computational biology has seen dramatic growth over
the past few years, in terms of newly available data, new
scientific questions and new challenges for learning and
inference. In particular, biological data is often relationally
structured and highly diverse, and thus requires combining multiple
weak evidence from heterogeneous sources. These sources include
sequenced genomes of a variety of organisms, gene expression data
from multiple technologies, protein sequence and 3D structural
data, protein interaction data, gene ontology and pathway
databases, genetic variation data (such as SNPs), high-content
phenotypic screening data, and an enormous
amount of text data in the biological and medical literature. These
new types of scientific and clinical problems require novel
supervised and unsupervised learning approaches that can use these
growing resources.

The workshop will host presentations of emerging problems and
machine learning techniques in computational biology. We encourage
contributions describing either progress on new bioinformatics
problems or work on established problems using methods that are
substantially different from standard approaches. Kernel methods,
graphical models, semi-supervised approaches, feature selection
and other techniques applied to relevant bioinformatics problems
would all be appropriate for the workshop.


Researchers interested in contributing should upload an extended
abstract of 4 pages in PDF format to the MLCB submission web site

by Oct 25, 2010, 11:59pm (Samoa time).

No special style is required. Authors may use the NIPS style file, but
are also free to use other styles as long as they use standard font
size (11 pt) and margins (1 in).

All submissions will be anonymously peer reviewed and will be
evaluated on the basis of their technical content. A strong
submission to the workshop typically presents a new learning method
that yields new biological insights, or applies an existing learning
method to a new biological problem. However, submissions that improve
upon existing methods for solving previously studied problems will
also be considered. Examples of research presented in previous years
can be found online at

Please note that accepted abstracts will be posted online at Authors may submit two versions of their abstract, a
longer version for review and a shorter version for posting to the web
page. In addition, we intent to make presentations be video taped and
published online as part of the website supported by

The workshop allows submissions of papers that are under review or
have been recently published in a conference or a journal. This is
done to encourage presentation of mature research projects that are
interesting to the community. The authors should clearly state any
overlapping published work at time of submission. Authors of
accepted abstracts will be invited to submit full length versions
of their contributions for publication in a special issue of BMC


Gunnar Rätsch,
Friedrich Miescher Laboratory of the Max Planck Society

Tomer Hertz,
Fred Hutchinson Cancer Research Center

Yanjun Qi,
Machine Learning Department, NEC Research

Jean-Philippe Vert,
Mines ParisTech, Institut Curie


Mathieu Blanchette, McGill University
Gal Chechik, Google Research
Florence d’Alche-Buc, Université d’Evry-Val d’Essonne, Genopole,
Eleazar Eskin, UC Los Angeles,
Brendan Frey (University of Toronto)
Alexander Hartemink (Duke University)
David Heckerman, Microsoft Research ,
Michael I. Jordan, UC Berkeley ,
Christina Leslie, Memorial Sloan-Kettering Cancer Research Center,
Michal Linial, The Hebrew University of Jerusalem ,
Quaid Morris, University of Toronto,
Klaus-Robert Müller, Fraunhofer FIRST ,
William Stafford Noble, Department of Genome Sciences, University of
Dana Pe’er, Columbia University ,
Uwe Ohler, Duke University ,
Alexander Schliep, Rutgers University,
Koji Tsuda, Computational Biology Research Center
Alexander Zien, LIFE Biosystems