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The NanoFolder sequence design approach is based on a Monte Carlo search in sequence space, utilizing a scoring function that includes a variety of terms.
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Because we utilize liberal thresholds for homology search of the RNA seq short reads, also weak hits contribute their score to the evidence measures of the functions.
Utilizing a scoring system that has been previously reported [ 23], RCTs were rated for methodological quality.
Then, target proteins are ranked according to the binding scores estimated by a scoring function.
In order to do so, we use six well-known methods A = CisGenome, B = MACS, C = PeakSeq, D = QuEST, E = SISSRs, and F = TRLocator and obtain the regions identified by each on a common ChIP-seq data set and utilize the tag count as a score function representing each method.
A score function needs to be selected.
Open image in new window Fig. 1 Scoring system P with: (a) score function (s_P), (b) rank function (r_P), and (c) rank-score characteristic (RSC) function (f_P).
The former utilizes the targeted learning algorithm as a black box to evaluate the usefulness of features according to their predictive performance [ 36], while the latter selects features according to a score function independent of the chosen learning algorithm [ 37].
We utilized a new normalized scoring function as well as network exclusion to improve the inference results.
The expression levels of SIRT1 and VEGF-C protein were calculated utilizing a semiquantitative scoring system.
The employed docking tools (GOLD and Glide) in this study utilize empirical scoring functions.
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