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You are an expert at identifying data availability statements in scientific text. A data availability statement describes where research data can be found or how it can be accessed.<|eot_id|><|start_header_id|>user<|end_header_id|>

Determine if the following text contains a data availability statement. Answer with 'yes' or 'no'.

Text: COGERE is capable to infer GRNs from unmatched data implying two advantages : ( i ) expression data can be obtained from different studies / measurements with identical experimental setups , ( ii ) detection of signals in at least a subset of experiments increases the robustness of the method against noise . COGERE balances the gene expression data by a condition - specific and individual - independent filtering of microarrays . The discriminatory power of the inference is sharpened as the variation within the conditions ( technical variation ) is reduced , whereas the differences between the conditions ( biological variation ) become more pronounced . We observed increased robustness of accuracy to detect context - specific effects due to differential TF - or miRNAmediated regulation in a benchmark with noisy expression data ( Supplementary Figures S2B and S6 ) . We remind that the performance assessment is based on simulated data . The in silico benchmark set is based on subnetworks from a human GRN with known interactions and thus holds similar types of structural properties and regulatory dynamics as realized in biological gene networks . The evaluation represents a simplified model of gene regulation . An in silico benchmark does not replace the careful evaluation in vivo , but enables a systematically and efficiently performance validation and comparison of prediction methods over multiple networks . Unfortunately , to date an elaborate in vivo data set composed of mRNA and miRNA expression for several conditions as well as the corresponding experimentally verified condition - specific GRN is not available for human or mouse . It is likely that methods that do not perform well in an in silico benchmark will perform even worse with real biological data ( 78 ) . In contrast to artificial data , linear correlation between a RG and a TG is a weak indicator of true condition - specific regulatory relationships in real expression measurements . This assumption is supported by a recent comprehensive and comparative evaluation of inference methods rating a two - way ANOVA - based approach best on the prediction of real GRNs from Escherichia coli and Saccharomyces cerevisiae expression data ( 53 ) .<|eot_id|><|start_header_id|>assistant<|end_header_id|>

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