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Firstly, we assigned MPSS tags as well as 70 mers of the oligonucleotide arrays to the current rice RAP2 gene predictions and selected genes that show significant expression levels (see Methods).

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This memory-based approach uses similarity, correlation or distance metrics to extract the predictions and select the best recommendations.

To remedy these problems, we focused on a limited set of MHC class II alleles for which good predictions exist, and selected those available in the TEPITOPE method [81].

(DOCX 11 MB) 13321_2014_38_MOESM5_ESM.zip Additional file 5: Table S4.: The Bayesian models for targets are shown in (a-e) and shows the target prediction charts and selected binders for targets with at least 3 examples.

LOO-CV again yielded relatively poor prediction errors and selected low numbers of k nearest neighbours as compared to the more stringent cross-validation schemes due to overfitting tendencies.

Using all these 6 modules (black, brown, green, pink, red, and yellow), we calculated all the pair expression correlations from target relations using sequence-based target prediction results and selected 254 miRNA-mRNA interactions by anti-correlation.

We explored target genes of individual miRNA by using the Diana-microT 3.0 target prediction program, and selected the genes with the miTG score ≧ 20 as the set of highly reliable targets.

To draw the sensitivity-specificity curves of the MetaProm prediction, we pooled predictions from the 10-fold cross-validation and ranked the MetaProm prediction scores (probabilities), and selected different cutoffs to get the Sensitivity-Specificity pairs at that cutoff.

Sample results are shown in this paper for stiffness as a function of shape factor, including comparison with finite element predictions, and for selected displacement, stress, and strain distributions.

To identify fusion/splitting events, we compared the two gene predictions using the cuffcompare program and selected the genes in one prediction overlapping to two or more genes in the other gene dataset.

The side chains for the selected backbone structures are built using the OSCAR side-chains prediction tool, then optimized and selected according to a combined energy of the OSCAR potential for flexible side-chains rotamers and CHARMM bond energies.

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