| pubmed-article:7491710 | rdf:type | pubmed:Citation | lld:pubmed |
| pubmed-article:7491710 | lifeskim:mentions | umls-concept:C1704675 | lld:lifeskim |
| pubmed-article:7491710 | lifeskim:mentions | umls-concept:C0684321 | lld:lifeskim |
| pubmed-article:7491710 | lifeskim:mentions | umls-concept:C0679932 | lld:lifeskim |
| pubmed-article:7491710 | lifeskim:mentions | umls-concept:C0936012 | lld:lifeskim |
| pubmed-article:7491710 | pubmed:issue | 10 | lld:pubmed |
| pubmed-article:7491710 | pubmed:dateCreated | 1996-1-4 | lld:pubmed |
| pubmed-article:7491710 | pubmed:abstractText | The rigorous estimation of a dissociation constant (Kb) for antagonists in functional assays has been sought by pharmacologists using a variety of techniques ever since the regression method of Arunlakshana and Schild in 1959. Here, Michael Lew and James Angus describe a simplified global regression method with improved accuracy compared to Schild analysis. The method is suitable for personal computers with standard graphing and statistical software. The accuracy of the predicted pKb values and confidence intervals has been tested by comparing examples of published data, and by mathematical (bootstrap) simulations. | lld:pubmed |
| pubmed-article:7491710 | pubmed:language | eng | lld:pubmed |
| pubmed-article:7491710 | pubmed:journal | http://linkedlifedata.com/r... | lld:pubmed |
| pubmed-article:7491710 | pubmed:citationSubset | IM | lld:pubmed |
| pubmed-article:7491710 | pubmed:status | MEDLINE | lld:pubmed |
| pubmed-article:7491710 | pubmed:month | Oct | lld:pubmed |
| pubmed-article:7491710 | pubmed:issn | 0165-6147 | lld:pubmed |
| pubmed-article:7491710 | pubmed:author | pubmed-author:AngusJ AJA | lld:pubmed |
| pubmed-article:7491710 | pubmed:author | pubmed-author:LewM JMJ | lld:pubmed |
| pubmed-article:7491710 | pubmed:issnType | Print | lld:pubmed |
| pubmed-article:7491710 | pubmed:volume | 16 | lld:pubmed |
| pubmed-article:7491710 | pubmed:owner | NLM | lld:pubmed |
| pubmed-article:7491710 | pubmed:authorsComplete | Y | lld:pubmed |
| pubmed-article:7491710 | pubmed:pagination | 328-37 | lld:pubmed |
| pubmed-article:7491710 | pubmed:dateRevised | 2006-11-15 | lld:pubmed |
| pubmed-article:7491710 | pubmed:meshHeading | pubmed-meshheading:7491710-... | lld:pubmed |
| pubmed-article:7491710 | pubmed:meshHeading | pubmed-meshheading:7491710-... | lld:pubmed |
| pubmed-article:7491710 | pubmed:meshHeading | pubmed-meshheading:7491710-... | lld:pubmed |
| pubmed-article:7491710 | pubmed:meshHeading | pubmed-meshheading:7491710-... | lld:pubmed |
| pubmed-article:7491710 | pubmed:meshHeading | pubmed-meshheading:7491710-... | lld:pubmed |
| pubmed-article:7491710 | pubmed:meshHeading | pubmed-meshheading:7491710-... | lld:pubmed |
| pubmed-article:7491710 | pubmed:year | 1995 | lld:pubmed |
| pubmed-article:7491710 | pubmed:articleTitle | Analysis of competitive agonist-antagonist interactions by nonlinear regression. | lld:pubmed |
| pubmed-article:7491710 | pubmed:affiliation | Department of Pharmacology, University of Melbourne, Parkville, Victoria, Australia. | lld:pubmed |
| pubmed-article:7491710 | pubmed:publicationType | Journal Article | lld:pubmed |
| pubmed-article:7491710 | pubmed:publicationType | Review | lld:pubmed |
| pubmed-article:7491710 | pubmed:publicationType | Research Support, Non-U.S. Gov't | lld:pubmed |
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