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The Empirical_Frequency model, which allowed the estimates of the stationary frequencies based on user dataset, was performed on the plant SDHs.
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The model selection sub-module is developed to select a proper learning algorithm and computing parameter set based on user's dataset via comparing/validating models and tuning parameters.
These datasets were generated based on user requests.
This is due to the fact that the non-aggregated dataset also contains user-specific features and is thus able to perform predictions based on user similarity, thus, yielding more accurate predictions.
Object rotation based on user attitude.
Content selection is based on user preference.
It first retrieves search results based on user queries.
Based on user recommendations, HIGH is considered an outbreak.
In order to extend the attainability of these two algorithms and to make them easy to use, we developed cGRNB (combinatorial Gene Regulatory Networks Builder): a web server for constructing combinatorial gene regulatory networks based on user-uploaded expression datasets.
With time series data, the time lag should be carefully selected based on users understanding of the dataset, in order to reveal the information embedded in time lags.
MobiQpons allows users to download coupons based on users' location and shopping interests.
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