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We use the terms predictions, computational inferences or hypotheses to refer to the outcomes of data-analytic algorithms.
In practice, this means that integrating various different cell-level data will be effective in prediction, which includes proteomics, ionomics, phenotypic data of mutants, bioinformatics predictions, computational simulations of pathways, and molecular dynamics of biomolecules.
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Each of these methods has its own strengths and limitations with respect to different aspects such as accuracy in detection and prediction, computational time, complexity and features captured.
We compare our promiscuity predictions with computational and experimental results obtained by Keiser et al.[4].
grubii genome and simultaneously validated short introns predicted from computational prediction pipelines.
LC, ZC, BZ LKM and JS performed in silico prediction and computational analyses.
As shown by these results, we have revealed the advantage of our AEP with respect to prediction and computational performance.
Despite remarkable progress in structure prediction methods, computational models often fall short in accuracy compared with experimental structures.
The technique has increased the available data on possible binding sites enormously, and raised the opportunity of better evaluating the prediction accuracy of the computational prediction methods.
Bioinformatics prediction was genome-wide and sequence-based computational predictions.
Besides the known PPIs, STRING also includes the predicted PPIs from the computational prediction methods.
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