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Metalearning is the study of principled methods that exploit metaknowledge to obtain efficient models and solutions by adapting machine learning and data mining processes (Brazdil et al. 2009 ).
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We adapted a machine learning approach that has been previously used to identify motifs specific to heart enhancers (Narlikar et al. 2010).
Table 1 summarizes the results returned by each machine learning method on the 50 runs.
For S. typhimurium, we also inferred this by our machine learning approach.
The world is quietly being reshaped by machine learning.
The proposed approach is inspired by machine learning.
Function is inferred by machine learning from co-behavior with known reference proteins.
The implementation of the algorithm was adapted from the WEKA machine learning package [ 52].
We compare results from seven machine learning methods adapted from the protein interaction extraction domain that employ part-of-speech, dependency and syntax features.
The proposed machine learning algorithm is able to adapt very quickly to changes in the RTT.
Methods from machine learning and statistics have been adapted to this problem.
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