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MHC class I binding probability was used to plot the distribution of the eluted peptides in accordance with the binding score for each breast cancer cell line.
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These binding probabilities were then used to identify condition-dependent changes in binding.
A stratified, multistage, probability design was used.
In each survey, probability sampling was used.
For computationally predicted binding sites, occurrences were taken to be those listed in the MEME output, and the "letter-probability-matrix" was used as the position weight matrix.
For computationally predicted binding-sites, occurrences were taken to be those listed in the MEME output, and the 'letter-probability-matrix' was used as position weight matrix.
A γ probability density function was used to determine the distribution of nearest binding site separations (30).
We also contribute a model of bimolecular binding probabilities that can be used in RW simulations.
was used to calculate probability values.
Multiple copies of the miR-312 binding sites were used to increase the probability of efficient miR-312 binding.
For the Viterbi Algorithm, the binding probabilities are set as the transition state probabilities, while the torsion state probabilities are set as the emission probabilities.
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