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To evaluate the possible genetic interrelationships between flour components and the sedimentation volume (SD), a doubled haploid (DH) population comprising 168 lines were used to identify the conditional quantitative trait loci (QTLs) for SD in three environments.
In this paper we adapted the method of Meinshausen and Bühlmann to binary data and used the LASSO for logistic regression to identify the conditional dependence structure.
Hence, we follow a sequential approach to first identify key parameters through global sensitivity analysis and then identify the conditional relationships among these parameters using the decision tree methods.
In stage 1, we identify the conditional methQTLs (influenced by the use of OCPs) that may result in a change of the DNA-M of specific CpG sites of the GATA3 gene.
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Optimal sCMP λ,ν,m) models are determined by comparing potential conditional sCMP models where m is assumed known and identifying the conditional model with the largest log-likelihood value.
This stage identifies the conditional methQTL (methylation quantitative trait loci) and the change in DNA-M once established can differentially regulate gene activity.
In addition to leveraging DNA methylation data to identify the latent signature of epigenetically silenced genes, we identified the conditional, bivalent associations of H3K4me3 and H3K27me3 with the H2A.Z histone variant.
We presented a novel method that can identify the candidate conditional specific regulatory motifs by employing kernel-based methods.
Starting from the joint posterior distribution in (17), it is possible to identify the full conditional posterior density of any parameter of interest by keeping the rest of them fixed.
In their experiments they showed that it is essential to have one risk preference elicitation task for measuring the curvature of the utility function, another task to identify the discount rate conditional on knowing the utility function, and then jointly estimate the structural model defined over the parameters of the utility function and discount rate.
To identify the RNAs influenced by the conditional deletion of Stat3Δ/Δ, RNAs isolated from alveolar epithelial type II cells of control and Stat3Δ/Δ mice were compared using Affymetrix murine genome MOE430 gene chips.
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