  Identification of human genes involved in the aging process is critical due
to the incidence of many diseases with age. A state-of-the-art approach for
this purpose infers a weighted dynamic aging-specific subnetwork by mapping
gene expression (GE) levels at different ages onto the protein-protein
interaction network (PPIN). Then, it analyzes this subnetwork in a supervised
manner by training a predictive model to learn how network topologies of known
aging- vs. non-aging-related genes change across ages. Finally, it uses the
trained model to predict novel aging-related genes. However, the best current
subnetwork resulting from this approach still yields suboptimal prediction
accuracy. This could be because it was inferred using outdated GE and PPIN
data. Here, we evaluate whether analyzing a weighted dynamic aging-specific
subnetwork inferred from newer GE and PPIN data improves prediction accuracy
upon analyzing the best current subnetwork inferred from outdated data.
Unexpectedly, we find that not to be the case. To understand this, we perform
aging-related pathway and Gene Ontology (GO) term enrichment analyses. We find
that the suboptimal prediction accuracy, regardless of which GE or PPIN data is
used, may be caused by the current knowledge about which genes are
aging-related being incomplete, or by the current methods for inferring or
analyzing an aging-specific subnetwork being unable to capture all of the
aging-related knowledge. These findings can potentially guide future directions
towards improving supervised prediction of aging-related genes via -omics data
integration.
