In this task, you are given a abstract of article and corresponding title of an article. Your task is to generate label "yes" if title is right for article, otherwise generate "no".

Q: Abstract: AIM To develop a reliable method for whole genome analysis of DNA methylation. MATERIALS & METHODS Genome-scale analysis of DNA methylation includes affinity-based approaches such as enrichment using methyl-CpG-binding proteins. One of these methods, the methylated-CpG island recovery assay (MIRA), is based on the high affinity of the MBD2b-MBD3L1 complex for CpG-methylated DNA. Here we provide a detailed description of MIRA and combine it with next generation sequencing platforms (MIRA-seq).
RESULTS We assessed the performance of MIRA-seq and compared the data with whole genome bisulfite sequencing.
CONCLUSION MIRA-seq is a reliable, genome-scale DNA methylation analysis platform for scoring DNA methylation differences at CpG-rich genomic regions. The method is not limited by primer or probe design and is cost effective.. Title: Controlling nuclear receptors: the circular logic of cofactor cycles

A: no
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Q: Abstract: Purpose. To demonstrate how correlations among predictor variables in a population pharmacokinetic model affect the ability to discern which covariates should enter into the structural pharmacokinetic model. Methods. Monte Carlo simulation was used to generate multiple-dose concentration-time data similar to that seen in a Phase III clinical trial. The drugs' pharmacokinetics were dependent on two covariates. Five data sets were simulated with increasing correlation between the two covariates. All data sets were analyzed using NONMEM both with and without inclusion of the covariates in the structural pharmacokinetic model. Summary measures for ill-conditioning and sensitivity analysis were used to examine how increasing correlation among covariates affects the accuracy and precision of the parameter estimates. Results. When covariates were included in the structural pharmacokinetic model and the correlation between covariates increased, the standard error of the parameter estimates increased and the value of parameter estimates themselves became increasingly biased. When the correlation between predictor variables was 0.75, the standard errors of the parameter estimates were too large to declare statistical significance. Conclusions. Correlations among predictor variables greater than 0.5 when entered into the model simultaneously should be a warning to researchers because the (1) the accuracy of the parameter estimates themselves may be biased and (2) the precision of the estimates may be inflated due to ill-conditioning.. Title: Gastric juice miR-129 as a potential biomarker for screening gastric cancer

A: no
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Q: Abstract: The estrogen receptor is the master transcriptional regulator of breast cancer phenotype and the archetype of a molecular therapeutic target. We mapped all estrogen receptor and RNA polymerase II binding sites on a genome-wide scale, identifying the authentic cis binding sites and target genes, in breast cancer cells. Combining this unique resource with gene expression data demonstrates distinct temporal mechanisms of estrogen-mediated gene regulation, particularly in the case of estrogen-suppressed genes. Furthermore, this resource has allowed the identification of cis-regulatory sites in previously unexplored regions of the genome and the cooperating transcription factors underlying estrogen signaling in breast cancer.. Title: Genome-wide analysis of estrogen receptor binding sites

A:
yes
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