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• W2951567107 abstract "We present a novel algorithm for the design of crossing experiments. The algorithm identifies a set of individuals (a 'crossing-set') from a larger pool of potential crossing-sets by maximizing the diversity of traits of interest, for example, maximizing the range of genetic and geographic distances between individuals included in the crossing-set. To calculate diversity, we use the mean nearest neighbor distance of crosses plotted in trait space. We implement our algorithm on a real dataset of Neurospora crassa strains, using the genetic and geographic distances between potential crosses as a two-dimensional trait space. In simulated mating experiments, crossing-sets selected by our algorithm provide better estimates of underlying parameter values than randomly chosen crossing-sets." @default.
• W2951567107 created "2019-06-27" @default.
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• W2951567107 date "2015-09-30" @default.
• W2951567107 modified "2023-10-12" @default.
• W2951567107 title "Selection of pairings reaching evenly across the data (SPREAD): A simple algorithm to design maximally informative fully crossed mating experiments" @default.
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• W2951567107 doi "https://doi.org/10.1038/hdy.2015.88" @default.
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