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For each consensus, 30 40 copies were isolated from the genome to produce a second and more robust consensus sequence.
To address this, we use the quantile transformation (Eq. 12) to define a more robust consensus network as follows Consensus q, ij (A (1), A (2),...) = Quant q, ij (A (1), A (2),...)...
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For each network, 1000 bootstrap replicates were performed and summarized to obtain more robust and accurate consensus networks.
In order to obtain a more robust list of discriminators, a consensus gene list of 244 genes with median rank less than 700 was generated, as described in the methods section, with the majority of the genes from the Golub-score ranked list being present in the consensus list.
The performance of consensus clustering is more robust, novel, stable, consistent, and out-perform Wards' method in case of using ALOGP fingerprint.
To obtain a more robust list of prognostic discriminators, a consensus gene list was created.
All participants were involved with pre-departure training for students in some way; however, there was consensus that a more robust and coherent approach would be in service of both students and those providing this service.
We would like to add a word of caution regarding consensus phylogenies: there is a body of evidence indicating that the "supermatrix" approach (concatenated alignments) provides more robust phylogenetic reconstructions compared to the "supertree" (consensus) methods[ 72- 75].
By using ECFP_4 fingerprint, consensus clustering methods provide more robust, stable, and consistent clustering and close to the Wards clustering results.
In this sense the profile distance results in a more computational efficient estimator and leads in contrast to a consensus approach to a more robust formulation.
For the first time, we demonstrate that this large-scale consensus QA approach is more robust and accurate than any individual quality method by integrating their strength together.
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