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Although the supermatrix approach relies on the well-established methodology of inferring gene trees, there exist many pitfalls that limit its application to larger analyses on a genomic scale.
Note that our concerns pertain to all meta-analyses employing this methodology of inferring behaviour change method effectiveness from differences in effect size; the papers we cite above are simple illustrations, and by no means particularly flawed.
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We introduce a new methodology for inferring the accuracy of computational simulations through the practice of solution verification.
However, the usefulness of the new methodology for inferring biodiversity patterns from non-microbial community samples which were collected using a standardized protocol as it is typically applied in ecological and conservation surveys and monitoring programs (e.g. [37], [49]), has rarely been tested to date (e.g. [6], [19], [20]).
Place-conditioning tests thus offer a general, yet powerful, methodology for inferring an animal's affective state, irrespective of the direct impact of the UCS.
Hennig also proposed a methodology for inferring such phylogenies.
Details about the methodology for inferring the tidal parameters are given in Andrioli et al. (2009).
From the observed microbiome community structures, and using a previously published methodology for inferring metabolomic data from microbial community structures [ 50], we predicted the metagenomes of microbiomes, expressed as community enzyme function profiles.
Results from the application of mixture Bayesian networks substantially augmented the understanding of gene networks and demonstrated the added-value of this methodology to infer gene networks.
In this study, we concentrated on the application of this methodology to infer gene networks using microarray data, and demonstrated how the interpretation of parameter estimates and function thereof offered new insights into the relationships between genes.
To summarize, we have developed a methodology to infer pairs of co-evolving genes.
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