Communications of the ACM
Discrete Optimization Algorithms with Pascal Programs
Discrete Optimization Algorithms with Pascal Programs
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
MultiMap: An Expert System for Automated Genetic Linkage Mapping
Proceedings of the 1st International Conference on Intelligent Systems for Molecular Biology
CARTHAGENE: Constructing and Joining Maximum Likelihood Genetic Maps
Proceedings of the 5th International Conference on Intelligent Systems for Molecular Biology
Pairing Transitive Closure and Reduction to Efficiently Reason about Partially Ordered Events
AI*IA '99 Proceedings of the 6th Congress of the Italian Association for Artificial Intelligence on Advances in Artificial Intelligence
Fast and high precision algorithms for optimization in large-scale genomic problems
Computational Biology and Chemistry
Consensus Genetic Maps as Median Orders from Inconsistent Sources
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
A Phylogenetic Approach to Genetic Map Refinement
RECOMB-CG '08 Proceedings of the international workshop on Comparative Genomics
Accurate Construction of Consensus Genetic Maps via Integer Linear Programming
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Inferring gene orders from gene maps using the breakpoint distance
RCG'06 Proceedings of the RECOMB 2006 international conference on Comparative Genomics
Finding consensus Bayesian network structures
Journal of Artificial Intelligence Research
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A genetic map is an ordering of genetic markers constructed from genetic linkage data for use in linkage studies and experimental design. While traditional methods have focused on constructing maps from a single population study, increasingly maps are generated for multiple lines and populations of the same organism. For example, in crop plants, where the genetic variability is high, researchers have created maps for many populations. In the face of these new data, we address the increasingly important problem of generating a consensus map 驴 an ordering of all markers in the various population studies. In our method, each input map is treated as a partial order on a set of markers. To find the most consistent order shared between maps, we model the partial orders as directed graphs. We create an aggregate by merginging the transitive closure of the input graphs and taking the transitive reduction of the result. In this process, cycles may need to be broken to resolve inconsistencies between the inputs. The cycle breaking problem is NP-hard, but the problem size depends upon the scope of the inconsistency between the input graphs, which will be local if the input graphs are from closely related organisms. We present results of running the resulting software on maps generated from seven populations of the crop plant Zea Mays.