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Another recent advance in this area was the introduction of the concept of Granger causality [4] which is well known in economics for causal inference on time series data [5], [6].
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These methods apply graphical modeling methods on time-series data and invoke the notion of 'Granger causality' to make assertions on causality through inference on time-lagged effects.
We apply our inference approach on time series transcriptomic data of 6 genes and 6 time points from an HMEC cell line following application of epidermal growth factor (EGF) and were able to generate a BN with a biologically plausible singleton attractor structure and satisfying the experimentally observed transitions.
Its cross-sectional design could limit the inference on the time sequence of the association between MD and nutritional status.
As this study had a cross sectional design, we are restricted in terms of making an inference on the time sequence of all associations.
Two immediate benefits of performing inference on serological time series are the ability to reconstruct complete disease dynamics – i.e. the dynamics of both symptomatic and asymptomatic individuals – and the avoidance of a reporting parameter.
For example, Alvarez et al proposed seven trajectory based indices which are suitable for fuzzy inference on real-time dynamic vulnerability [6].
It is to be noted that a single dynamic programming (DP) method was used in our previous work on the completion and inference of time-independent networks [ 20], whereas a double DP method is employed here in order to cope with time-varying networks.
If animals produce different types of sounds, or if cue rate varies over time, there are clear benefits in focusing inference on cues or time periods for which the variability across animals is low.
Different models and modeling techniques can be used for making inference on the same time series data.
This study makes a number of key contributions to the challenging problem of network inference based solely on time-course data.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com