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These have to be observed experimentally to calibrate the computational fitness correlates.
Defining computational fitness correlates for these models is usually only a minor addition that is based on biological intuition.
Data: Computational fitness correlates data depends on the successful completion of level 4. Observable fitness correlates data can be readily generated by well-established experimental protocols for measuring properties such as survival, fecundity, or growth rates.
Successes: Computational fitness correlates that match experimental observations include the effects of lethal gene knockouts on growth rates in yeast that can be predicted from flux balance analysis in over 90% of all cases [ 11, 13, 14, 9].
The challenge is to find mutants that differ enough from the wild type to result in significant observable differences and that are characterised well enough at the molecular level to allow the prediction of computational fitness correlates.
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Here we develop statistical and computational methods for fitting fitness data from mutation combinatorial networks to three simple models: additive, multiplicative and stickbreaking.
In order to decrease the computational complexity in fitness function we used Eq. (14).
Obviously, the limited sample size of SNP data affects their computational accuracies of fitness functions and hence hinders their further applications.
In this work we propose an algorithm to evolve a robust speech representation, using a dynamic data selection method for reducing the computational cost of the fitness computation while improving the generalisation capabilities.
The algorithm's performances are illustrated and improvement in prediction quality is demonstrated both in terms of goodness of fitness and computational effort.
In the case of image processing applications, the high computational cost of the fitness function that is evaluated repeatedly can cause training time to be relatively long.
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