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- W1499844511 abstract "This paper explores the use of information and computational learning theory for the multi-model comparison of hybrid modeling frameworks describing cellular response to environmental cues. The hybrid framework consists of a mechanistic sub-model (describing early intracellular signaling mechanisms) that is used to inform a downstream empirical sub-model. Since the exclusive consideration of a mechanistic model describing intracellular signaling dynamics is often insufficient to predict downstream cell behaviors, an empirical model is incorporated in the framework to fill the gap. We propose a methodology for the selection of a particular integrated multi-scale mechanistic-empirical model based on the tradeoff between linear correlation and agreement with beliefs about the underlying true process. First, experimental input conditions are used in a mechanistic sub-model to stochastically generate an intermediate signaling dataset; effectively augmenting the input data space. Then the most appropriate mechanistic sub-model is selected from various candidates based on its ability to explain the output response data under the appropriate precision. We develop a methodology using the Pearson's Product Moment Correlation Coefficient as a metric for comparison. In addition, the distribution of the correlation coefficient is compared against the distribution asserted using beliefs about the underlying process. We apply the approach to a T-Cell immune response problem." @default.
- W1499844511 created "2016-06-24" @default.
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- W1499844511 date "2015-07-01" @default.
- W1499844511 modified "2023-09-22" @default.
- W1499844511 title "Multi-model selection of integrated mechanistic-empirical models describing T-cell response" @default.
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- W1499844511 doi "https://doi.org/10.1109/acc.2015.7171814" @default.
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