Table 3 shows the temporally ranked actors accompanied by their static ranks, and the high-ranked static actors with their temporal ranks, both with lifetime (mathcal {T}=) [2005 2011].
The rank r1 is the spatial rank, and the rank r2 is the temporal rank.
These observations might explain the high temporal rank of J1 06) in the knowledge mobilization network.
19 CSD-associated ptiO2 response analysis was also performed using the matching ECoG event criteria with differentiation between the 1st, 2nd, 3rd, and 4th or later temporal rank of the ptiO2 response within the cluster.
In summary, the approaches we described use a developed unified dynamic test framework that includes SETI with statistical significance testing, ranking temporal genes by AR(1) modeling and posterior probability of autocorrelation parameter, and HMM to classify temporal dynamic patterns.
All human protein-coding genes were sorted according to these median ranks to estimate the temporal order of replication.
Regarding comorbidities, these were coded merely as absent/present, there was no severity ranking or temporal perspective.
The above inference method provides for continuous trajectory regression involved with timing evolution features to rank temporal genes statistically for a given pattern, as well as such genes' temporal differential expression patterns among conditions.
The ranks were assigned to the priority classes according to the temporal order of interactions in vivo.
We propose a dynamic network evaluation framework for considering temporal variability, which ranks stations under different starting days with a fixed time window (1-year, 2-year, and 5-year).
It is then a minimal version of the ego-network, as it involves the node and its direct neighbors.1 There is very little structural information available and hence, we use temporal information to rank pairs among the neighbors of an ego node.
