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Using our algorithm, we predicted a set of high-confidence ORF targets and selected seven miRNA-target pairs from among these for experimental validation.

Then, we predicted a set of high-potency natural products by using the iPPI-likeness score based on a docking score-weighted model.

In each model, a unidimensional latent variable (i.e., reading ability) predicted a set of manifest variables (i.e., reading items).

Next, we downloaded the HIV proteome from the Los Alamos HIV database and predicted a set of potential HLA-A2 epitopes.

We predicted a set of 52,492 high-confidence DDIs to carry out cross-species comparison of DDI conservation in eight model species including human, mouse, Drosophila, C. elegans, yeast, Plasmodium, E. coli and Arabidopsis.

Computational analysis of the integrated multi-omic datasets predicted a set of putative virulence proteins.

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Instead of predicting the business type, our prediction algorithm, P-DPA, predicts a set of stores that could be visited by a targeted client, and then, uses the predicted stores to generate a list of advertisements for the targeted client.

Our method provides a simple, computationally efficient means of predicting a set of residues that mediate allosteric communication.

For a fixed time interval and number of debris interactions n, the optimized solution predicts a set of n thrust impulses, n debris captures, and n debris ejections.

Moreover, the overall perceptual score (OPS), the target-related perceptual score (TPS), the interference-related perceptual score (IPS) and the artifacts-related perceptual score (APS) objective measures have been used with the aim of predicting a set of subjective scores.

In short, stochastic modeling is a quantitative description of a natural phenomenon which predicts a set of possible outcomes weighted by their likelihoods or probabilities (Karlin and Taylor 1998).

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