Nonconstructive tools for proving polynomial-time decidability
Journal of the ACM (JACM)
Approximating clique is almost NP-complete (preliminary version)
SFCS '91 Proceedings of the 32nd annual symposium on Foundations of computer science
Regular Article: On search, decision, and the efficiency of polynomial-time algorithms
Proceedings of the 30th IEEE symposium on Foundations of computer science
Cluster analysis and mathematical programming
Mathematical Programming: Series A and B - Special issue: papers from ismp97, the 16th international symposium on mathematical programming, Lausanne EPFL
An algorithm for clustering cDNAs for gene expression analysis
RECOMB '99 Proceedings of the third annual international conference on Computational molecular biology
Tissue classification with gene expression profiles
RECOMB '00 Proceedings of the fourth annual international conference on Computational molecular biology
Genome-Scale Computational Approaches to Memory-Intensive Applications in Systems Biology
SC '05 Proceedings of the 2005 ACM/IEEE conference on Supercomputing
Scalable Parallel Algorithms for FPT Problems
Algorithmica
Fast, effective vertex cover kernelization: a tale of two algorithms
AICCSA '05 Proceedings of the ACS/IEEE 2005 International Conference on Computer Systems and Applications
Combinatorial genetic regulatory network analysis tools for high throughput transcriptomic data
RECOMB'05 Proceedings of the 2005 joint annual satellite conference on Systems biology and regulatory genomics
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The tools of molecular biology and the evolving tools of genomics can now be exploited to study the genetic regulatory mechanisms that control cellular responses to a wide variety of stimuli. These responses are highly complex, and involve many genes and gene products. The main objectives of this paper are to describe a novel research program centered on understanding these responses by developing powerful graph algorithms that generate distilled gene sets, producing high performance implementations utilizing cutting-edge platforms, employing these implementations to identify gene sets suggestive of coregulation, and performing sequence analysis and genomic data mining to examine, winnow and highlight the most promising gene sets for more detailed investigation. As a case study, we describe our work aimed at elucidating genetic regulatory mechanisms that control cellular responses to low dose ionizing radiation (IR). We use genome-scale gene expression data after IR exposure in vivo to identify the pathways that are activated or repressed as a tissue responds to the radiation insult. Knowledge of these pathways should help clarify and interpret physiological responses to IR, which will advance our understanding of how IR exposures pose an increased risk to human health.