The phrase "allocation of genes" is correct and usable in written English.
It can be used in contexts related to genetics, biology, or discussions about the distribution of genetic traits among populations or individuals.
Example: "The allocation of genes in this population has significant implications for its overall health and adaptability."
Alternatives: "distribution of genes" or "assignment of genes."
Informally, our approach may be considered as a 'correlated clustering' model, in which the allocation of genes to clusters in one dataset has an influence on the allocation of genes to clusters in another.
A formal approach for comparing the allocation of genes to clusters is to calculate the ARI between each pair of clustering partitions (Hubert and Arabie, 1985; Rand, 1971).
Let A ∈ {0,1} N×M be a binary matrix such that P-A ∈ {0,1} N×M, where A represents a potential allocation of genes to pathways that we use in our methods.
Although they do so in different ways, both the ARI and the dataset association parameters quantify the degree of similarity between the allocation of genes to clusters in pairs of datasets.
Multiple assays of similar conditions or tissues (albeit with strain and rearing differences, see above) will proportionally shift the focus of the map towards those conditions or tissues; less space will be available for the allocation of genes into clusters based on other expression characteristics.
Let Z ji be the allocation of gene i in context j to a cluster.
Let D,E ∈ B N×M be gene to pathway allocations, as described in the Notation section, such that P-D ∈ B N×M and P-E ∈ B N×M, i.e. D and E are allocations of genes.
The number of allocations of genes to pathways is determined by the number of expressed genes and the number of pathways in which the expressed multi-membership genes participate.
As expected, the mean fitness value also shows an increase as the number of allocations of genes to pathways grows, with highly significant correlation value of 0.9769, 0.9777 and 0.9770 for the hill climbing, simulated annealing and genetic algorithm respectively.
Here the number and size of operons and the number of che genes are kept constant, but the che genes are shuffled, so that the allocation of che genes to operons is randomised.
The spatial scale of resistance gene deployment strategies runs from regional allocation of resistance genes through on-farm variation generated by few large fields versus multiple small fields, to intercropping within individual fields and the use of random mixtures in an individual crop (Papaïx et al. 2011).
