Abstract
Constructing populations with pre-specified characteristics is a fundamental problem in population genetics and other applied areas. We present a novel non-generative approach that deconstructs the desired population into essential local constraints and then builds the output bottom-up. This is achieved using primarily best-fit techniques from discrete methods, which ensures accuracy of the output. Also, the algorithms are fast, i.e., linear, or even sublinear, in the size of the output. The non-generative approach also results in high sensitivity in the algotihms. Since the accuracy and sensitivity of the population simulation is critical to the quality of the output of the applications that use them, we believe that these algorithms will provide a strong foundation to the methods in these studies.
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Haiminen, N., Lebreton, C., Parida, L. (2014). Best-Fit in Linear Time for Non-generative Population Simulation. In: Brown, D., Morgenstern, B. (eds) Algorithms in Bioinformatics. WABI 2014. Lecture Notes in Computer Science(), vol 8701. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-44753-6_19
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DOI: https://doi.org/10.1007/978-3-662-44753-6_19
Publisher Name: Springer, Berlin, Heidelberg
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