A comparison of methods for quantifying prediction uncertainty in systems biology

Revision as of 12:59, 25 February 2020 by Bwday (talk | contribs) (Outcome O2)

Citation

Villaverde, Alejandro F., et al. "A comparison of methods for quantifying prediction uncertainty in systems biology." IFAC-PapersOnLine 52.26 (2019): 45-51.

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Summary

Three methods for quantifying prediction uncertainty in ODE models are assessed. Here, prediction uncertainty does not refer to estimated parameters, but to the uncertatinty of state trajectories. The three methods are: Fisher Information Matrix (FIM), Prediction Posetrior (PP), Ensemble Consensus (ENS).

Study outcomes

Outcome O1

For a small, fully-observed ODE model (α-pinene), all three methods yield nearly same results consistent with the known true trajectories. For a larger, only-partially observed ODE model (JAK2/STAT5), PP and ENS yield better accuracy than FIM. However, even for PP and ENS, confidence levels do not cover the truth.

Outcome O2

The computational cost of the three models is differing, especially for large problems: FIM (small), ENS (intermediate), PP (high)

Outcome O3

The computational cost of the three models is differing, especially for large problems: FIM (small), ENS (intermediate), PP (high)

Further outcomes

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Study design and evidence level

General aspects

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Design for Outcome O1

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Design for Outcome O2

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Design for Outcome O

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Further comments and aspects

References

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