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PhyloDetect [ 37] is one of the few analysis methods that deals explicitly with the genomic similarity between taxonomically related organisms, and the consequent tendency of some probes to cross-hybridize to multiple organisms.
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But the price for this is that there few general analysis methods can be applied to it; they would need to be modified, something that is not always possible.
Few pathway analysis methods have been published that use hierarchical modelling for identifying associated pathways.
Few sensitivity analysis methods using pattern mixture models and selection models are available for this scenario [ 23, 24].
While metrics that evaluate this chain of complex activities have been designed, there are few quantitative analysis methods able to jointly analyze these metrics and take advantage of the multivariate nature of the data.
Motivation: Over the past few years several pathway analysis methods have been proposed for exploring and enhancing the analysis of genome-wide association data.
The methods described here are highly applicable to many other morphology-based tissue scoring techniques that pathologists perform routinely on slides stained with H&E, for which few other effective automated analysis methods exist.
Whilst we identify disruptive selection candidate regions in all lake flocks, we find similar trends, across analysis methods, towards fewer disruptive selection candidate regions and fewer adaptive trait/candidate loci associations in the more polluted lakes.
Nevertheless, few hashing-based histopathological image analysis methods perform feature fusion, despite the fact that it is a common practice to improve image retrieval performance.
The proposed approach is generic and does not require assumptions on FLC Ss rules or structures as is common with the few existing fuzzy systems controllability analysis methods in the literature.
So far, very few model-based analysis method have been proposed to validate or invalidate models, especially for nonlinear and spatially distributed models [ 6, 71, 72].
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