PubMed Health⌕ Search
Home/Archive/PubMed

PubMed · 3383425

Standardization by reference methods: a German viewpoint.

E S Schmidt·F W Schmidt
View original source ↗Export metadata ↓

Abstract

The problems of applying optimized as well as standardized methods to the determination of enzyme activities in modern clinical laboratories are described. The failure of the so-called 'New Concept of Reference Method Values' in the quality assessment of enzyme assays is evidenced, and the advantages of standardized methods rather than only standard materials is demonstrated. Finally, the great similarities among European national recommended methods for enzyme assay is emphasized by a comparison of results obtained in estimating 5 enzymes.

Explore related subjects

EnzymesReference StandardsJournal Article

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

E S Schmidt, F W Schmidt. 1988-03-31. Standardization by reference methods: a German viewpoint.. https://doi.org/10.1016/0009-8981(88)90352-x

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related citations

PubMed2026

How not to be seen: predicting unseen enzyme functions using contrastive learning.

MOTIVATION: Predicting enzyme function from its sequence is still an unsolved problem in the life sciences. Moreover, with the explosion of annotated genome data, we are inundated with potential enzymatic sequences that have not yet been biochemically characterized. While it is not possible to assign a not-yet-existing label to such a sequence, there is high value in placing the sequence as accurately as possible in known function space. Doing so can help provide more accurate falsifiable hypotheses for experimentalists wishing to characterize enzymes from specific functional families. RESULTS: Here we present a contrastive learning algorithm for predicting enzyme function from sequence. Our method, EnzPlacer, predicts the third, second, and first EC numbers for a protein whose fourth EC number is not in the training corpus. This novel prediction mechanism accurately places a protein sequence within a narrowed-down functional context, even if the precise function remains unknown. AVAILABILITY AND IMPLEMENTATION: EnzPlacer and data is available at https://github.com/drxiangma/EnzPlacer under a GPL3 license.

Enzymes↗