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Since the FANTOM3 data are assembled from diverse sources (tissues, experimental conditions, various laboratories, etc), the simplest and reasonable model for the FANTOM3 data is a collection of diverse RNA families similar to the composition of our training dataset.
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A part of our training.
Moreover, when the proportion of trainees in different disciplines changes significantly, some educational resources, including the composition of training instructors, training conditions, and training facilities, are not adjusted accordingly in a timely manner.
And just like a fingerprint, the composition of our "microbiome" is unique to each of us.
Due to the composition of the training sets, we expected our classifiers to be biased towards identifying mainly cassette (skipped) exons.
This might be a result of the larger training data set and especially the composition of the negative training data used in our study.
However, since the composition of the training set remains the same in this method, it is not certain that the resultant model is optimal as there may be a bias in descriptor selection.
Three different system settings were considered: the family of descriptors used to represent the facial features; the composition of the training set in terms of representatives of the classes to be predicted; the scaling of the input variables in the features vector to be supplied to the classifier.
the family of descriptors used to represent the facial features; the composition of the training set in terms of representatives of the classes to be predicted; the scaling of the input variables in the features vector to be supplied to the classifier.
We applied an unsupervised approach that is not dependent on the composition of a training set.
More information about the structure of the BNs and the composition of the training sets can be found in the help web page of the server or in the original publication describing the method [7].
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