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HK managed the data handling.
The framework provides data handling, logging, and error handling.
Each application is informed by algorithmic data and, increasingly, machine learning.
Algorithmic, data analytic, machine learning and numerical methods which support the modeling and analysis of optimization problems are encouraged.
Think of algorithmic data mining and statistical computation, robotics, and the automation to be found in any modern biological laboratory.
Data handling and statistical analyses.
In parallel, the triple matrix data pattern analyzer CLASSIF1 [ 18] was used as an algorithmic data mining approach.
An algorithmic approach to handle imbalanced data distributions is based on ensembles of classifiers.
Algorithmic improvements allow handling of much larger datasets in reasonable time.
Data were handled as dichotomized categorical data.
As described in the Methods section, algorithmic significance handles the problem of multiple testing using a penalty that takes very little time to compute.
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