20 research outputs found

    PhID: An Open-Access Integrated Pharmacology Interactions Database for Drugs, Targets, Diseases, Genes, Side-Effects, and Pathways

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    The current network pharmacology study encountered a bottleneck with a lot of public data scattered in different databases. There is a lack of an open-access and consolidated platform that integrates this information for systemic research. To address this issue, we have developed PhID, an integrated pharmacology database which integrates >400 000 pharmacology elements (drug, target, disease, gene, side-effect, and pathway) and >200 000 element interactions in branches of public databases. PhID has three major applications: (1) assisting scientists searching through the overwhelming amount of pharmacology element interaction data by names, public IDs, molecule structures, or molecular substructures; (2) helping visualizing pharmacology elements and their interactions with a web-based network graph; and (3) providing prediction of drug–target interactions through two modules: PreDPI-ki and FIM, by which users can predict drug–target interactions of PhID entities or some drug–target pairs of their own interest. To get a systems-level understanding of drug action and disease complexity, PhID as a network pharmacology tool was established from the perspective of data layer, visualization layer, and prediction model layer to present information untapped by current databases

    One KEGG reaction example, S-Adenosyl-L-methionine+L-Tryptophan< = >S-Adenosyl-L-homocysteine+Abrine (R00683, ‘enzyme not yet characterized’), and its closest training KEGG reaction, S-Adenosyl-L-methionine+Serotonin< = >S-Adenosyl-L-homocysteine+N-Methylserotonin (R02910), for EC assignment prediction.

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    <p>One KEGG reaction example, S-Adenosyl-L-methionine+L-Tryptophan< = >S-Adenosyl-L-homocysteine+Abrine (R00683, ‘enzyme not yet characterized’), and its closest training KEGG reaction, S-Adenosyl-L-methionine+Serotonin< = >S-Adenosyl-L-homocysteine+N-Methylserotonin (R02910), for EC assignment prediction.</p

    Comparisons of several EC assignment methods by considering their method basis, if they are automatic for a whole reaction, and if there is a web server available.

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    <p>Comparisons of several EC assignment methods by considering their method basis, if they are automatic for a whole reaction, and if there is a web server available.</p

    Cross-validation accuracy performance over different EC levels.

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    <p>With a selected fingerprint length at 3, the number of reactions and the cross-validation accuracies will vary from EC1 to EC6.</p

    Crassula barbata

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    <p>Cross-validation accuracies of reaction difference fingerprints with different lengths.</p
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