Optimal Comparability Editing for Molecular Diagnostics
Author Information
Author(s): Sebastian Böcker, Sebastian Briesemeister, Gunnar W. Klau
Primary Institution: Institut für Informatik, Friedrich-Schiller-Universität, Jena, Germany
Hypothesis
How can we transform a directed graph representing patient subgroups into a transitive graph with minimal edge modifications?
Conclusion
The exact algorithms developed are significantly more efficient than previous heuristic approaches and can enumerate all optimal solutions.
Supporting Evidence
- The algorithms compute provably optimal solutions.
- The exact algorithms are capable of enumerating all optimal solutions.
- The performance of the algorithms significantly outperforms previous heuristic approaches.
Takeaway
This study shows how to fix messy data about patients' diseases by organizing it into a clearer structure using special computer methods.
Methodology
The study uses fixed-parameter algorithms and integer linear programming to solve the comparability editing problem.
Limitations
The algorithms may not reduce the size of the graph significantly, and constructing a problem kernel remains an open problem.
Digital Object Identifier (DOI)
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