ArticleGenomics, proteomics & bioinformatics2005
Predicting the coupling specificity of GPCRs to G-proteins by support vector machines.
Article in Genomics, proteomics & bioinformatics, 2005. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- A novel approach for prediction of tacrolimus blood concentration in liver transplantation patients in the intensive care unit through support vector regression.Critical care (London, England) · 2007Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
G-protein coupled receptors (GPCRs) represent one of the most important classes of drug targets for pharmaceutical industry and play important roles in cellular signal transduction. Predicting the coupling specificity of GPCRs to G-proteins is vital for further understanding the mechanism of signal transduction and the function of the receptors within a cell, which can provide new clues for pharmaceutical research and development. In this study, the features of amino acid compositions and physiochemical properties of the full-length GPCR sequences have been analyzed and extracted. Based on these features, classifiers have been developed to predict the coupling specificity of GPCRs to G-proteins using support vector machines. The testing results show that this method could obtain better prediction accuracy.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.