We start from a given training set of specificity groups and identify SDPs using the SDPlight procedure.
As further detailed in the next section, since our aim was to identify new DoS following an unbiased data-driven systematic approach, we did not impose any restrictions in the set of specificity masks that can be found; instead, we explore a large set of possible specificity masks and let the system evolve and find those showing the best discriminatory capabilities.
This type of approach, known in machine learning as learning classifier systems (Lanzi et al., 2000), enables the selection of the best-performing set of specificity masks starting from a large initial set of random masks by following three consecutive steps.
SDPgroup is an iterative procedure to augment a pre-defined training set of specificity groups with new sequences from the MSA, and SDPtree is a wrapping procedure that multiply picks a random training set for SDPgroup and constructs the best clustering pattern.
When multiple trigger sequences targeted the same gene, as for Dm3, overlapping sets of specificities were abrogated.
To further estimate the extent of the distance between the transcriptome and the translatome of each experiment, we compared the semantic specificity measures with a reference distribution, calculated as the set of semantic specificities between the transcriptome of each dataset pair and the transcriptome of all the other datasets.
Our set of relative specificity constants is more consistent with the results of initial rate experiments than is the set that the authors obtained by fitting a pseudo-first order model to their data.
First, we analyzed total splenic RNA using real-time RT-PCR with a primer-probe set of broad specificity for MLV-related RNA sequences.
f Selectivity expressed at 95%and75%5% sensitivity, with sensitivity measured on the taxonomic set of Metazoa, specificity measured on set of 3,526,115 C. elegans genomic hairpins.
