Sentence examples for inference strongly from inspiring English sources
October 2004 Published in Nature : Our paper, "Performance of maximum parsimony and likelihood phylogenetics when evolution is heterogeneous". We show that heterogeneity in the evolutionary process can make current methods for phylogenetic inference strongly biased.
The more favoured notion that emerged from these difficulties besetting the analogical inference (strongly influenced by Wittgenstein's writings on the nature of first and third person psychological statements) was that criterial evidence could deal with the problem in a way that avoided the problems besetting the analogical inference.
Phylogenetic analysis using Bayesian inference strongly supports the orthology of all identified genes (supplementary fig. S2).
This comprehensive survey of individual cases of protein interaction discovery through computation-driven experiment or experimentally derived computational inference strongly suggests that gene fusion detection can be a valuable tool for modern, high-throughput proteomics [ 1].
NPI is a statistical approach based on few assumptions, with inferences strongly based on data and with uncertainty quantified via lower and upper probabilities.
Such inference is strongly supported by comparing the PL spectra between the ns- and fs-PLD CIGS thin films at 15 K.
Reliability of ancestral inference is strongly dependent on distances among nodes [69].
Most methods of phylogenetic inference rely strongly on probabilistic models of sequence evolution, and neither directly detect nor correct for convergent evolution [23].
This inference is strongly reinforced by the results of present study, where most of the identified CSPs correspond to well-defined clades in the phylogenetic trees.
This conversion inference is strongly supported by a P-value of 2.31 * 10-5; it also agrees with other studies of evolutionary relationships in the β-globin cluster (e.g. [ 28, 30]).
Thus, the involvement of these genes in the same network, as predicted by our network inference algorithm, is strongly supported by knowledge of regulatory relationships already established in experimental data.
