When only restricted errors are allowed, N e, p B ) is not computed using all the symbols in Σ '.
Different ways to compute the errors allowed in the approximate patterns (restricted errors, alphabet range weighted errors and pattern length adaptive errors) can also be used.
Even in this case, considering the sheer number of errors instead of the percentage, when we restrict errors to contacts (Err-1) we obtain more accurate reconstructions.
However, if only restricted errors with z = 1 are allowed, D5 can only be substituted by { N5} leading to 1-CCC-Bicluster B = ({G1, G2},{C2–C5}).
In general, when restricted errors are considered, the allowed substitutions for any symbol A ij are in the set, where is the position of A ij in and z is a value in that specifies the number of neighbors both to the left and to the right of that are considered valid errors.
However, if we are specially interested in minimizing the also problematic effects of potential discretization errors, introduced due to poor choice of discretization thresholds or number of symbols, we can consider restricted errors, that is, substitutions of the symbols A ij by the lexicographically closer symbols (neighbors) in.
When the alphabet Σ used to discretize the data has many symbols, we can either restrict the errors allowed in the approximate patterns to a neighborhood around the symbol, or to consider alphabet range weighted errors.
Misinterpreting phylogenetic trees is not restricted to errors related to taxa relatedness; phylogenetic tree misinterpretations are diverse and pose significant barriers to understanding evolution (Meir et al., 2007).
We used a nuclear localized Venus fluorescent protein reporter in an effort to restrict measurement errors caused by cell size- or shape-dependent variations.
See also additional file 5: 1_ccc_biclusters_vs_ccc_biclusters for a detailed comparison between the 47 highly significant 1-CCC-Biclusters discovered by 1-CCC-Biclustering restricted to errors in the 1-neighborhood of the symbols in the alphabet Σ = { D, N, U} and the 16 highly significant CCC-Biclusters found by CCC-Biclustering and analyzed by Madeira et al. [ 9].
