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Therefore, the development of computational techniques for inferring the interaction network of the drug entities and its kinetic parameters from observational data is raising great interest in the scientific community of pharmacologists.
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They often comprise of a very large number of entities, the nodes, and the interest lies in inferring the interactions between these entities, the edges, from relatively limited data.
For each pair (A i, B i) of query domain instances that meets the stringent alignment threshold to a template interacting domain instance pair (A, B), we infer the interaction interface residues in (A i, B i) as the residues that are aligned to the interaction interface residues in (A, B).
Incorporating the similarity between drugs and between targets to infer the interaction of untested drug target pairs is the essence of the read-across methodology [2].
Then we use the cluster centers (means) as representatives of each cluster and apply the Partial Granger causality to infer the interaction pattern.
Bifunctional proteins that catalyze two steps in a metabolic pathway can be used to infer the interaction of orthologous proteins that exist as separate entities in other genomes.
In this paper we explored Interaction-Identifier method to infer the interaction patterns between multiple ESC regulators.
Similar to MRA and LMML, SBRA infers the interaction strengths in the form of a weight matrix W [ 7].
To infer the interaction rules that are used in mixed-species flocking, our analysis followed five steps.
If we could extract context vectors from the known interaction in the specific disease dataset, we will be able to infer the interaction using context vectors of the condition specific interaction.
For example, when we chose 0.5 as the probability threshold to infer the interaction type, >99% of positive DTIs predicted by our algorithm were consistent (i.e. <1% of DTIs were predicted as both direct and indirect interactions).
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CEO of Professional Science Editing for Scientists @ prosciediting.com