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The results from these simulations indicate that while the predictive value of inferred terms is vastly increased by use of simulations, the inferred data still has to be compared to ground truth only via careful validation. Shmulewitz et al. used simulations to model the inherent noise of scRNA-seq data sources and found that predictability was significantly increased under a larger number of events [2].
Due to this aspect linked with assuming one-to-one relationship between genes and expression values, this assumption is most likely not always true. Power, et al. applied a modelling approach as part of the gates network inference tool GIN [92] and included a likelihood term approximating non-normality of counts [8].
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While in principle the results are similar to usual hypotheses tests, differences in the distribution of the data and the derivation of the mean and variance make that this test is not generally directly applicable. Beer et al. also considered this problem of volatility and heuristics under uncertainty [318].
When CASP data are compared with experimental results, it is not uncommon to see disagreements between methods and characteristics of the network reconstruction. This is not unexpected, as the methods are applied to data that are in many respects incomplete and noisy, and the details of the network function are never completely revealed. It also becomes evident that with more and more experimental data and improved methods that are adaptable to the data at hand, more and more structure and function can be learned from the available data. " d2c66b5586
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