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Chemogenomic techniques connect the chemical and biological domains to establish ligand and target relationships not evident from the individual disciplines.
Moreover, this approach is not scalable to corpora with a large number of target relationships or where the target relationships cannot be specified in advance [7].
Therefore, a kinase-specific SEA model is useful and reliable (due to its high prediction accuracy) for capturing target relationships within the kinase families.
MicroRNA:target relationships are different.
mirDIP integrates prediction databases to elucidate accurate microRNA:target relationships.
Since microRNA binding is sequence-based but possibly structure-specific, work in this area has resulted in multiple databases storing predicted microRNA:target relationships computed using diverse algorithms.
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Gunther, S. et al. SuperTarget and Matador: resources for exploring drug-target relationships.
Chen, K. & Rajewsky, N. Deep conservation of microRNA-target relationships and 3′UTR motifs in vertebrates, flies, and nematodes.
Variance component estimates suggested that unique perceiver-target relationships account for a majority of variance in ratings of the DT.
Pérez-Nueno, V. I., Souchet, M., Karaboga, A. S. & Ritchie, D. W. GESSE: Predicting Drug Side Effects from Drug-Target Relationships.
For examining miRNA-target relationships using cumulative distribution functions, the predicted miRNA targets were obtained from TargetScan release 7.1, and filtered to eliminate predicted targets with poor total context scores.
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