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SGI Best
Paper and Best Student Paper Finalists Chosen
The ISMB2003
program committee, chaired by Gene Myers, professor of electrical
engineering and computer sciences at the University of California,
Berkeley, has chosen the finalists for this year's SGI Best Paper
and Best Student Paper awards. The winners in both categories will
be selected at ISMB2003.
The program
committee and senior reviewers for the conference provided the initial
nominations. The committee’s co-chairs then chose the finalists
after reviewing all nominations. The winners will be selected by
a subset of the senior review committee based on the written paper
as well as oral presentation at the conference. The awards will
be presented Wednesday, June 2 at 17:20 during ISMB2003.
SGI Best
Paper candidates
- Roded Sharan, Ivan Ovcharenko, Asa Ben-Hur, and Richard Karp:
CREME: A Framework for Identifying Cis-Regulatory Modules in Human-Mouse
Conserved Segments
- Saurabh Sinha, Erik van Nimwegen, and Eric Siggia: A Probabilistic
Method to Detect Regulatory Modules
- Eran Segal, Roman Yelensky, and Daphne Koller: Genome-wide
Discovery of Transcriptional Modules from DNA Sequence and Gene
Expression (also nominated for SGI Best Student Paper)
SGI Best
Student Paper candidates
- Maureen Heymans and Ambuj Singh: Deriving phylogenetic trees
from the similarity analysis of metabolic pathways
- Orla Osullivan, Marc Zehnder, Des Higgins, Philipp Bucher,
Aurelien Grosdidier, and Notredame Cedric: APDB: A Novel Measure
for Benchmarking Sequence Alignment Methods without Reference Alignments
- Yoshihiro Yamanishi, Jean-Philippe Vert, Akihior Nakaya,
and Minoru Kanehisa: Extraction of Correlated Gene Clusters from
Multiple Genomic Data by Generalized Kernel Canonical Correlation
Analysis
- Eran Segal, Roman Yelensky, and Daphne Koller: Genome-wide
Discovery of Transcriptional Modules from DNA Sequence and Gene
Expression (also nominated for SGI Best Paper)
- Eran Segal, Haidong Wang, and Daphe Koller: Discovering Molecular
Pathways from Protein Interaction and Gene Expression Data
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