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Our approach uses a generalized version of a two-layer undirected graphical model, called restricted Boltzmann machine (RBM) (Hinton and Salakhutdinov, 2006), to represent a multidimensional DTI network, which encodes different types of DTIs.
Compared with other prediction approaches, our RBM model can capture not only the correlations of drug-target pairs in a DTI network but also the correlations of different types of DTIs.
Tests on two public databases show that our restricted Boltzmann machine model can effectively capture the latent features of a DTI network and achieve excellent performance on predicting different types of DTIs, with the area under precision-recall curve up to 89.6.
a DrugBank DTI network was downloaded.
d 49 Mtb targets from DTI network were prioritized using combined evidence approach.
DB03705 and DB04196 interact with single targets in the DTI network while DB04047 interacts with only 2 targets in the network indicating low promiscuity in the DTI network suggesting a high specificity for the target.
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Researchers developed network-based inference model (NBI) to deduce the potential interactions among unapproved DTPs in given DTI networks and further confirmed them from in vitro assays [ 7].
RepTB utilizes Gene Ontology (GO) based drug-target interaction (DTIs) network to compute association scores for identification of new DTIs.
While structural links in DTI brain networks can capture the number of neural fibers connecting different brain regions.
On combining the DrugBank DTIs and GO mapping DTIs, the RepTB network contained 26,404 edges with 6630 drug and 4083 target nodes.
Some of these techniques are capable of carrying out multimodal visualization involving magnetic resonance imaging (MRI), fiber tracts as obtained from DTI and overlaying network connections.
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CEO of Professional Science Editing for Scientists @ prosciediting.com