About: iGPCR-Drug: A Web Server for Predicting Interaction between GPCRs and Drugs in Cellular Networking

Facets (new session)
Description
Metadata
Settings
- owl:sameAs
- Inference Rule:

About: iGPCR-Drug: A Web Server for Predicting Interaction between GPCRs and Drugs in Cellular Networking Goto Sponge NotDistinct Permalink

An Entity of Type : schema:ScholarlyArticle, within Data Space : covidontheweb.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
title	iGPCR-Drug: A Web Server for Predicting Interaction between GPCRs and Drugs in Cellular Networking
Creator	Wang, P Chou, K-C Xiao, X Min, J-L
source	PMC
abstract	Involved in many diseases such as cancer, diabetes, neurodegenerative, inflammatory and respiratory disorders, G-protein-coupled receptors (GPCRs) are among the most frequent targets of therapeutic drugs. It is time-consuming and expensive to determine whether a drug and a GPCR are to interact with each other in a cellular network purely by means of experimental techniques. Although some computational methods were developed in this regard based on the knowledge of the 3D (dimensional) structure of protein, unfortunately their usage is quite limited because the 3D structures for most GPCRs are still unknown. To overcome the situation, a sequence-based classifier, called “iGPCR-drug”, was developed to predict the interactions between GPCRs and drugs in cellular networking. In the predictor, the drug compound is formulated by a 2D (dimensional) fingerprint via a 256D vector, GPCR by the PseAAC (pseudo amino acid composition) generated with the grey model theory, and the prediction engine is operated by the fuzzy K-nearest neighbour algorithm. Moreover, a user-friendly web-server for iGPCR-drug was established at http://www.jci-bioinfo.cn/iGPCR-Drug/. For the convenience of most experimental scientists, a step-by-step guide is provided on how to use the web-server to get the desired results without the need to follow the complicated math equations presented in this paper just for its integrity. The overall success rate achieved by iGPCR-drug via the jackknife test was 85.5%, which is remarkably higher than the rate by the existing peer method developed in 2010 although no web server was ever established for it. It is anticipated that iGPCR-Drug may become a useful high throughput tool for both basic research and drug development, and that the approach presented here can also be extended to study other drug – target interaction networks.
has issue date	2013-08-27(xsd:dateTime)
bibo:doi	10.1371/journal.pone.0072234
bibo:pmid	24015221
has license	cc-by
sha1sum (hex)	db7b6440cfdfd2bbf71c22361d4ea606a16d4643
schema:url	https://doi.org/10.1371/journal.pone.0072234
resource representing a document's title	iGPCR-Drug: A Web Server for Predicting Interaction between GPCRs and Drugs in Cellular Networking
has PubMed Central identifier	PMC3754978
has PubMed identifier	24015221
schema:publication	PLoS One
resource representing a document's body	covid:db7b6440cfdfd2bbf71c22361d4ea606a16d4643#body_text
is schema:about of	named entity 'PREDICTING' named entity 'NETWORKING' named entity 'higher' named entity 'experimental' named entity 'test' »more»

Faceted Search & Find service v1.13.91 as of Mar 24 2020

Alternative Linked Data Documents: Sponger | ODE Content Formats:

RDF

ODATA

Microdata

About

OpenLink Virtuoso version 07.20.3229 as of Jul 10 2020, on Linux (x86_64-pc-linux-gnu), Single-Server Edition (94 GB total memory)
Data on this page belongs to its respective rights holders.
Virtuoso Faceted Browser Copyright © 2009-2025 OpenLink Software