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Many analysis approaches consider relating allelic, haplotypic, or genotypic information to a trait through use of extensions of traditional analysis techniques, such as contingency-table analysis, regression methods, and analysis-of-variance techniques.
Many analysis approaches have been developed in recent years and have attempted to exploit functional information and multivariate analyses to provide answers about molecular systems functioning.
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This is because many cluster analysis approaches multiply the complexity of a chosen similarity search method by the number of compounds in the dataset.
A many body analysis approach is used to study the two- and three-body contributions in the total interaction energy of complexes.
Unfortunately today, many visualization and analysis approaches are being developed independently.
Many microarray data analysis approaches are based on case-control study designs like comparing treated and untreated cells or matched disease and control tissues.
Many 'meta-analysis' approaches have considered such details of expression data [ 28, 29], but they are applicable to raw data only.
These new results and conclusions have implications for many commonly used genomic analysis approaches, and for the evolution of high-fidelity RNA polymerase II transcriptional initiation in eukaryotes.
Finally, a challenge with all visual analysis approaches is that many tools run for hours or days to operate on genome-scale data, making them impractical for interactive use.
Although many articles present case studies, such comparative case analysis would require more explicit use of formal case study analysis approaches than is common.
To account for these factors the process will use a sensitivity analysis approach to consider many possible design cases, this approach involves the creation of a large number of randomly created cases, all with different input values and giving different factors of safety.
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