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In this work we report, for the first time, the use of a supervised molecular dynamics approach to explore the possible protein-peptide binding pathways within a timescale reduced up to three orders of magnitude compared with classical molecular dynamics.
On the other hand, it was found that the use of a supervised method (such as PLS) clearly improved the performance of the classifier.
In addition, the use of a supervised analysis method (support vector machines) made it possible to correctly classify samples.
Ramesh and Yu [ 18] proposed the use of a supervised machine learning method called conditional random fields (CRFs) to automatically identify discourse connectives in biomedical texts.
By use of a supervised classification method, we found that using only nine genes the MSI/MSS classification was extremely robust, reaching a sensitivity and specificity that was comparable to microsatellite analysis.
To compare conditions in order to extract highly differentially expressed genes, our strategy consists on the use of a supervised grouping using T-test as a statistical approach to extract significantly variable genes.
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To mitigate this challenge, we examine the use of a partially supervised learning classification method to automate the process.
Our system makes use of a distantly supervised approach, implementing the multi-instance multi-label system of Surdeanu et al. (2012).
More contemporaneous data (2004 - 2005) from Vancouver, Canada explored changes in injection practices associated with use of a medically supervised safer injection facility (SIF) [ 22].
Therefore we may conclude that such a model can provide relevant information and thus - considering the multi-factorial aetiology of fall events - could be valuable not as a replacement of professional assessment scores and tools, but as a supplementary method which is feasible to be used outside of a supervised clinical environment.
At this point, use of a semi-supervised method would have overcome the deficit.
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