  Mass-spectrometry technologies are widely used in the fields of ionomics and
metabolomics to simultaneously profile at the genome scale intracellular
concentrations of e.g. amino acids or elements. Short profiles of molecular or
sub-molecular features are intrinsically non-Gaussian and may reveal patterns
of correlations that reflect the system nature of the cell biochemistry and
biology. Here we introduce two profile similarity measures that enforce
information from the empirical covariance matrix of the data, the Mahalanobis
cosine and the hybrid-Mahalanobis cosine. We evaluate the performance of these
similarity measures in the task of inferring and integrating genetic networks
from omics data by analysing experimental datasets derived from the ionome and
the metabolome of the model organism S. cerevisiae, and several large curated
databases of genetic annotations. The proposed covariance-based similarity
measures can in general recover known and predicted associations between genes
better than the commonly used Pearson's correlation and the standard cosine
similarity. The choice of which of the two measures to recommend depends upon
whether the focus is on extracting genetic associations at a global or local
genetic network scale.
