  Gene expression (GE) data capture valuable condition-specific information
("condition" can mean a biological process, disease stage, age, patient, etc.)
However, GE analyses ignore physical interactions between gene products, i.e.,
proteins. Since proteins function by interacting with each other, and since
biological networks (BNs) capture these interactions, BN analyses are
promising. However, current BN data fail to capture condition-specific
information. Recently, GE and BN data have been integrated using network
propagation (NP) to infer condition-specific BNs. However, existing NP-based
studies result in a static condition-specific subnetwork, even though cellular
processes are dynamic. A dynamic process of our interest is human aging. We use
prominent existing NP methods in a new task of inferring a dynamic rather than
static condition-specific (aging-related) subnetwork. Then, we study evolution
of network structure with age - we identify proteins whose network positions
significantly change with age and predict them as new aging-related candidates.
We validate the predictions via e.g., functional enrichment analyses and
literature search. Dynamic network inference via NP yields higher prediction
quality than the only existing method for inferring a dynamic aging-related BN,
which does not use NP.
