Previous computational methods to quantify and visualize kinetic differential expression data of three or more lineages to identify reciprocally regulated genes have relied on clustering approaches and regression methods which have time as a factor, but have lacked methods which explicitly model temporal behavior.
If specific time subintervals are known to be relevant, they can be investigated increasing the number of time points in these subintervals of T. Model outputs (matrix Y) are then collected for each experiment considering the different time points of T, such that model temporal behaviors (i.e. model traces) can be derived.
A novel synchronization model namely Extended Real-Time Synchronization Model (E-RTSM) for modeling SMIL2.0 temporal behaviors is proposed in this paper.
The first back-end system is based on a MAP approach, and the two following systems are very similar but they model the temporal behavior in different ways: on a first step, the scores of both systems are conditioned using a multi-class logistic regression.
Multinomial logistic regression was used to model temporal trends in combined behaviors over the study period using S-MVPA/LST as the reference category and including year of survey as an ordinal variable.
In particular, we will first apply the hidden Markov model (HMM) to model spatio-temporal behavior patterns and use the expectation-maximization (EM) method to learn the parameters of the model.
Another intuitive method discussed by Ivaha, Al-Madfai, Higgs, and Ware [7] first models the temporal behaviors of crimes with time series models and then models the spatial behaviors given the predicted number of incidents at a certain time.
Modeling of the temporal behavior of disease read-outs at short [ 63] or long [ 64] intervals can identify sub-phenotypes of NCDs.
In order to represent the immune response, PDEs based on a previous model of the innate response [ 42] have been employed to model the spatial and temporal behavior of the following components: S.
Usually, the temporal behavior is modeled as a time series, where time explains the behavior of the relevant variable.
