This work is general and we have applied it to many datasets of animal's vocalisations (e.g. cetaceans, mice, birds).
Unfortunately, such available graphs are today limited to small post-mortem datasets (only 50 70 nodes) of rat [5], cat [6], [7] and monkey [8] brains, whereas larger datasets of animal and human brains are missing [9].
Datasets composed of animals genotyped with the Illumina BovineHD chip (Illumina, San Diego, California, USA) were created.
Training datasets consisted of animals of either single lines, or a combination of two or all three lines, and had 30 508 to 45 974 segregating single nucleotide polymorphisms.
Their unsupervised machine learning method could potentially be extended to any dataset of animal vocalizations.
The performance of the methodology is studies via simulations and using a biological dataset of animal communication signals comprising 43 groups of electric signals recorded from tropical South American electric knife fishes.
The general conclusion is that correlations between genomic predictions from 50k SNP and deregressed EPD in independent datasets of related animals are 0.5-0.7 0.5-0.7
In this paper we first describe our datasets of yeast, animal and plant proteins with their orthologs, divergence and other features we used for classification, and the classifiers we employed.
For satellite telemetry datasets, the number of animals per record was always one even though one individual tag may have multiple observations each with a different location, depending on how often data were recorded and published.
We also analyze three large datasets (nuclear proteins of animals, mitochondrial proteins of mammals, and plastid proteins of plants), and find the optimal number of components of the MBL model to be two for all datasets, indicating that this model is preferred over the standard homogeneous model.
