20 research outputs found
PhID: An Open-Access Integrated Pharmacology Interactions Database for Drugs, Targets, Diseases, Genes, Side-Effects, and Pathways
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
Correct and incorrect results made over different reaction distances.
<p>Correct and incorrect results made over different reaction distances.</p
Incorrect prediction KEGG reaction examples on different substrate specificities.
<p>Incorrect prediction KEGG reaction examples on different substrate specificities.</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>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.
<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.
<p>With a selected fingerprint length at 3, the number of reactions and the cross-validation accuracies will vary from EC1 to EC6.</p
Incorrect prediction KEGG reaction examples on inter-molecular and intra-molecular transformations.
<p>Incorrect prediction KEGG reaction examples on inter-molecular and intra-molecular transformations.</p
Additional file 2 of SynBioTools: a one-stop facility for searching and selecting synthetic biology tools
Additional file 2. The code zip file on extracting tabular information from papers by paring the full-text XML file
Crassula barbata
<p>Cross-validation accuracies of reaction difference fingerprints with different lengths.</p
Additional file 3 of SynBioTools: a one-stop facility for searching and selecting synthetic biology tools
Additional file 3. The list of reviews used for the tool and tool information extraction