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The multiple regression method is trained once for all and it is not possible to add a new "style characteristic" without having to train the whole model again.
The automated method is trained to mimic an expert reader in distinguishing hotspots due to metastases from those caused by factors such as degenerative disease or fractures.
The method is trained and tested on a dataset containing 1 239 TFs with known DNA-binding specificity, and used to predict specific DNA target motifs for 645 TFs with high accuracy.
The method is trained and tested on human-mouse and human-dog sequence alignments.
As our method is trained on interaction data of natural PDZ domains, it may not do well at quantitative interaction prediction involving synthetic PDZ domains that have multiple mutations not found in our training data.
Our method is trained to recognize these regions based on the presence of coding, conserved non-coding elements (assumed to be enriched for regulatory regions) and their putative interactions.
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The method was trained using the CASMI training spectra, along with additional merged spectra from GNPS [32] and MassBank [33].
The machine learning method was trained on 7352 spectra (4564 compounds) downloaded from GNPS [32] and MassBank [33].
The results obtained using SVM method are trained from the same supervised data used in semi-supervised approach, in green color.
The Bayesian method was trained for 200.000 cycles at maximum or when the likelihood improved less than 1e-4 per iteration, and we computed the classification error for each of the three methods.
This is somewhat expected because the method was trained for remote homolog recognition (structurally similar protein with less than 30% sequence identity, PREFAB benchmark).
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