The phrase "are suitable for modelling" is correct and usable in written English.
It can be used when discussing the appropriateness or effectiveness of certain items, methods, or data for the purpose of creating a model.
Example: "The collected data from the experiment are suitable for modelling the behavior of the system under study."
Alternatives: "are appropriate for modelling" or "are fit for modelling".
Among the modelling approaches, Markov models are suitable for modelling of dynamic system behaviour.
Two groups previously showed that fkrp MO-injected zebrafish embryos recapitulate α-dystroglycan glycosylation defect and thus are suitable for modelling human dystroglycanopathies (47, 48).
Generalized linear models were used to model costs ppm on HAART as they are suitable for modelling cost data, which tend to be skewed, without the need for transformation [ 25].
According to [ 4], neural networks are suitable for modelling human genetics studies due to the ability of handling large quantities of data with reasonable computation time as the scalability of data increases exponentially.
Additionally, although model systems may trigger valuable new lines of investigation, a major concern remains whether mice, or other genetic models such as fruitflies and worms, are suitable for modelling human disease in view of their vastly shorter lifespan compared to humans, as well as significant systems level differences in brain physiology.
One direction to consider is theoretical: exploring the properties of belief change operators that are suitable for modelling belief manipulation effectively.
The results show that the numerical methods are suitable for modeling air pollution levels at high resolution.
The studies that are suitable for modeling CAES has been available in [12, 13, 14, 15, 16].
More specifically, we show how visibility maps are suitable for modeling propagation phenomena more complex than specular reflections.
For an overview of different stochastic processes which are suitable for modeling the parameters estimated by VLBI, see Herring et al. (1990).
Neural networks with deviating argument, which are proposed in the model of recurrent neural networks by Akhmet et al. [41], are suitable for modeling situations in physics, economy, and biology.
