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These experimental results demonstrate the importance of the recurrent ICNV regions and the predicted key driver genes, especially global drivers in terms of significant impact on cancer cell viability, and thus validate our procedures for inferring CNV regions and identifying cancer driver genes in these inferred CNV regions.
We previously proposed efficient procedures for inferring biological network based on experimentally observed time-series data of mRNA or metabolites [ 7- 10] using S-system and real-coded genetic algorithms (RCGAs) [ 11] with a combination of uni-modal normal distribution crossover UNDX) [ 12] and minimal generation gap MGG) [ 13].
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Using the sample replicates, we propose a bootstrap procedure for inferring the significance of contributions on the leading dimensions by both the time-course experiments and the genes.
Now, we give a sketch of the second procedure for inferring graph entropy measures that results in obtaining partition-independent measures [63] [65].
To address this we developed a procedure for inferring the number of ancestries using AIC.
Overall, we have detailed a multistep procedure for inferring causal networks integrating phenotypic, genotypic, and transcriptomic data.
The procedure for inferring empirical FDR based on simulations described above has been implemented in the R package empiricalFDR.DESeq2, hosted within the Comprehensive R Archive Network (CRAN).
Here, we performed a multistep procedure for inferring causal networks underlying carcass fat deposition and muscularity in pigs using multi-omics data obtained from an F2 Duroc x Pietrain resource pig population.
A retrospective survey thus involves a standardized, structured questionnaire about past events, an accepted sampling method, and a statistical procedure for inferring about the general population from the sample's findings.
The level of annotation is however widely heterogeneous across species, making it crucial for any comparative analysis to carefully choose a set of species for which we can guarantee a good enough annotation, and a same procedure for inferring the metabolic network from the annotated genomes.
First, it separates the procedures of inferring haplotypes and imputing missing genotypes.
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