Parametric optimization of sequence alignment
SODA '92 Proceedings of the third annual ACM-SIAM symposium on Discrete algorithms
The complexity and approximability of finding maximum feasible subsystems of linear relations
Theoretical Computer Science
Fast and numerically stable parametric alignment of biosequences
RECOMB '97 Proceedings of the first annual international conference on Computational molecular biology
On the Complexity of Deriving Score Functions from Examples for Problems in Molecular Biology
ICALP '98 Proceedings of the 25th International Colloquium on Automata, Languages and Programming
Computational Biology at the Beginning of the Post-genomic Era
Informatics - 10 Years Back. 10 Years Ahead.
Computational Biology - Algorithms and More
ESA '00 Proceedings of the 8th Annual European Symposium on Algorithms
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Various bioinformatics problems require optimizing several different properties simultaneously. For example, in the protein threading problem, a linear scoring function combines the values for different properties of possible sequence-to-structure alignments into a single score to allow for unambigous optimization. In this context, an essential question is how each property should be weighted. As the native structures are known for some sequences, the implied partial ordering on optimal alignments may be used to adjust the weights. To resolve the arising interdependence of weights and computed solutions, we propose a novel approach: iterating the computation of solutions (here: threading alignments) given the weights and the estimation of optimal weights of the scoring function given these solutions via a systematic calibration method. We show that this procedure converges to structurally meaningful weights, that also lead to significantly improved performance on comprehensive test data sets as measured in different ways. The latter indicates that the performance of threading can be improved in general.