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The aim of LAF is to classify biological sequences and assign them to their taxonomic unit with the aid of a supervised machine learning paradigm [ 51] (see subsection Supervised machine learning and rule-based classification algorithms for more details).
This is referred to as "supervised" machine learning.
For this, we explored the possibilities of using supervised machine learning.
Our work does not rely on manual engineering or even supervised machine learning.
Fig. 1 Supervised machine learning workflow.
Features can be combined through a supervised machine learning approach.
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Semi-supervised machine learning methods have the features of both, integrating labeled and unlabeled training data.
At a very granular level, experimental methods are especially adept at assessing causality; supervised machine learning excels at prediction where non-supervised machine learning can decompose non-numerical stimuli into tags or attributes for further analysis.
To do this, we use a method of human-supervised machine learning that we think other companies could learn from.
Semi-supervised machine learning of preM ER+ patients revealed three groups with strikingly different outcomes.
The inference of the model is conceived as a supervised machine-learning task.
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