Sentence examples for this class of interactions from inspiring English sources

This class of interactions is particularly important in development, immunology, infection, and carcinogenesis.

Conceptual explanations for this class of interactions invoke information-filtering frameworks such as elite cues, confirmation bias, cultural cognition, biased assimilation, motivated reasoning, reinforcing spirals, and selective exposure [15, 30-39].

Concomitant causes such as combinatorial regulatory effects [4] or condition-specific activation of the TF-BS edges [11], [30] certainly play a role in the loss of relevance of this class of interactions.

However, our analyses show that this class of interactions is not only not overrepresented but in fact significantly underrepresented in the human PIN (supplementary table S7, Supplementary Material online).

Expanding the class of interactions from linear models to non-linear models is an important research topic.

Finally, we furthermore assume that in the class of interactions investigated, the intermediate state R*P cannot go down a monomeric activation route [ 28].

Herein, we describe the recent approaches developed to inhibit this class of protein protein interactions.

The results demonstrated the capability of the coupled model to analyse this class of soil structure interaction problems.

Explanations for this general class of interactions commonly invoke information-filtering processes, whether top-down as with elite cues (educated partisans more aware of positions taken by their political or media leaders) or bottom-up as with biased assimilation or motivated reasoning (educated partisans more actively acquire/reject information according to their prejudices).

Non-intrusive methods: In this class of methods, the interaction is based on the manipulation of the input/output files without modifications in the 3rd party solver.

The interactions between salicylate and tetracycline, salicylate and chloramphenicol, and sodium benzoate and tetracycline are all suppressive, but this class of models describes a range of interactions ranging from synergistic to suppressive, based on the interplay of induction benefit and drug toxicity.