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NK and DB developed the required software, created the dataset and evaluated the methods.
We ran the function prediction methods for sequences in this benchmark dataset and evaluated the method's prediction performances.
To accomplish this goal, this work applied machine learning (ML, see glossary in Additional file 1) modeling to a large longitudinal dataset and evaluated the method's ability to identify multiple, equally predictive sets of variables.
We also extracted information from the publicly accessible 'The Cancer Genome Atlas (TCGA)' RNA-sequencing dataset and evaluated the expression of YKL40 in normal adjacent prostate tissue vs prostate tumour tissues.
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We identified regions of significant methylation enrichment (peaks) from the WT and DKO MeDIP-Seq datasets and evaluated the CpG composition of sequences underlying these peaks.
We first defined large-head spines as spines whose size was among the largest 5% of all measured spines in the each of the pooled datasets and evaluated the size distribution among the 3 groups.
In this study, we used the NCI60, CCLE, and CGP pharmacogenomic datasets and evaluated the effectiveness of different computational approaches in deriving multi-omic signatures predictive of drug response.
We conducted a meta-analysis of 1000 E.coli datasets and evaluated the consistency within results of gene set enrichment methods, based on the regulatory interactions found in the transcriptional network of E.coli.
In our experimental study, we applied the methods RAxML (Stamatakis, 2006), NOTUNG (Durand et al., 2006), TreeFix (Wu et al., 2013), MowgliNNI (Nguyen et al., 2012), AnGST (David and Alm, 2011) and our new method TreeFix-DTL, to simulated datasets and evaluated the accuracy of the inferred gene trees.
The chapters introduces each software package, provide details on how to get access to the software, demonstrate how the software can be used to perform geomorphometric analysis using the common Baranja Hill dataset, and evaluate the software's strengths and weaknesses.
It is defined as a rank of the value in a dataset as a percentage of the dataset, and evaluates the relative standing of a value within a dataset.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com