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This step was taken because the training set consisted of mostly environmentally relevant compounds.
We build this model based on SVM because the training set can be optimized by reweighting the importance of each sample used for training in SVM model.
Genome-wide bioinformatic predictions face the challenge of a high false positive rate, mostly because the training set of known imprinted genes is small, and we do not know all the signals driving tissue- and time-specificity of imprinting [2], [3].
This is because the training set is used twice: first for the estimation of and then for the estimation of.
Because the training set samples and the test set samples in the other datasets are from the same experiments, we chose not to normalize these data to avoid any loss of information.
Because the training set samples and the test set samples in the prostate cancer dataset are from two different experiments, and because discrepancies in microarray intensity exist between the two sets of samples, we normalized both the training set and the test set.
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This is important because although the training set contained 33 (18 %) spine patients, the Scandinavian external validation set contained only patients with extremity metastases.
*It was unnecessary to apply the Gaussian SVM to the one-vs-one case because the training sets were always found to be linearly separable using Ho-Kashyap.
The HV procedure we applied might have resulted in too low R values because the training sets included only 20 sites, which likely resulted in less robust models than the developed models that were based on 40 sites.
Because hairpins in the training set often contain flanking sequences around the precise pre-miRNAs, we annotated the exact position of the pre-miRNA in each hairpin in the training set according to that of the corresponding miRNA [ 8, 9, 20].
capillosus ATCC 29799, B. coprosuis DSM 18011, B. finegoldii DSM 17565, B. fragilis YCH46, B. helcogenes P 36-108, and B. salanitronis DSM 18170), because they skew the training set of TF-binding sites and thus decrease the sensitivity of the TF-binding site (TFBS) recognition rule.
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