Annals of Operations Research - Special issue on Tabu search
Integrating Multiple-Platform Expression Data through Gene Set Features
ISBRA '09 Proceedings of the 5th International Symposium on Bioinformatics Research and Applications
Computational Intelligence: An Introduction
Computational Intelligence: An Introduction
Introduction to Algorithms, Third Edition
Introduction to Algorithms, Third Edition
Comparative evaluation of set-level techniques in microarray classification
ISBRA'11 Proceedings of the 7th international conference on Bioinformatics research and applications
GSGS: A Computational Approach to Reconstruct Signaling Pathway Structures from Gene Sets
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
QSEA for fuzzy subgraph querying of KEGG pathways
Proceedings of the ACM Conference on Bioinformatics, Computational Biology and Biomedicine
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With the increasing availability of gene sets, novel approaches that focus on reconstructing networks from gene sets are of interest. Currently, few computational approaches explore the search space of candidate networks using a parallel search. As such, novel methods that employ search agents are needed to help better escape local optima. In particular, gene sets may model signal transduction events, which refer to linear chains or cascades of reactions starting at the cell membrane and ending at the cell nucleus. These events may be indirectly observed as a set of unordered and overlapping gene sets. Thus, the underlying goal is to reverse engineer the order information within each gene set to reconstruct the underlying source network. To achieve this goal, we developed the Gene Set Cultural Algorithm to discover the true order of the gene sets and to reconstruct the underlying network. In a proof of concept study, we show that the Gene Set Cultural Algorithm can satisfactorily reconstruct three E. coli networks from the DREAM initiative using simulated and unordered gene sets as the input.