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These results demonstrate the deep-learning techniques can serve as powerful modeling techniques for modeling complex biological data.
The recent success demonstrates that deep learning methods are well suited for modeling complex biological data to support drug discovery and toxicological research.
The proposed stochastic models meet the accuracy and computational speed requirements for modeling complex biological processes.
Nagasaki et al. [ 10] have proposed an object-oriented Petri nets called hybrid functional Petri net with extension (HFPNe) for modeling complex biological systems.
Regarding to the fact that current biological structures and functions are optimized by evolution over a long time, optimization is a natural and powerful tool for modeling complex biological problems.
For modeling complex biological processes intuitively, we are required to deal with various kinds of biological information, e.g. the density of molecules, the number of molecules, sequences, molecular modifications, binding location, localization of molecules, etc.
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Holcombe et al. note the strength of using ABM to model complex biological systems.
These studies determine that ANN approach is reasonably effective in modelling complex biological phenomenon.
ABMs can be used to model complex biological systems in which the behavior of heterogeneous individual components, stochastic events, and/or spatiotemporal considerations are important.
Systems biology aims to model complex biological interactions by integrating information from interdisciplinary fields in a holistic manner (holism instead of the more traditional reductionism).
This modelling mechanism provides a sensible way of dealing with problems associated with state explosion, which afflict many efforts at modelling complex biological systems [ 9, 29].
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