Validation-based model selection for 13C metabolic flux analysis with uncertain measurement errors
Fig 10
How prediction uncertainty can be used to assess the novelty in the validation data.
(A) If there is too little novelty in the validation data, differences between estimation data and validation data will typically be smaller than the prediction and measurement uncertainty. (B) If there is too much novelty in the validation data, there is no information about the corresponding MIDs, and the prediction uncertainty will be large, approaching [0,1]. (C) An ideal design of validation data is thus to have well-determined predictions that are different compared to the estimation data. To be sure that there really is new information, one should also check that the new fluxes generate linearly independent EMU basis vectors (Section 2.4).