Computational Genomics
The Akaike Information Criterion (AIC) is a statistical measure used to compare the goodness of fit of different models while penalizing for complexity. It helps in model selection by balancing the trade-off between accuracy and simplicity, where lower AIC values indicate a better model fit relative to others. This criterion is particularly useful in phylogenetic analysis to identify the most appropriate tree topology based on the given data.
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