Probabilistic Random Forest improves bioactivity predictions close to the classification threshold b...
Probabilistic Random Forest improves bioactivity predictions close to the classification threshold by taking into account experimental uncertainty
About this item
Full title
Author / Creator
Publisher
Cham: Springer International Publishing
Journal title
Language
English
Formats
Publication information
Publisher
Cham: Springer International Publishing
Subjects
More information
Scope and Contents
Contents
Measurements of protein–ligand interactions have reproducibility limits due to experimental errors. Any model based on such assays will consequentially have such unavoidable errors influencing their performance which should ideally be factored into modelling and output predictions, such as the actual standard deviation of experimental measurements...
Alternative Titles
Full title
Probabilistic Random Forest improves bioactivity predictions close to the classification threshold by taking into account experimental uncertainty
Authors, Artists and Contributors
Identifiers
Primary Identifiers
Record Identifier
TN_cdi_doaj_primary_oai_doaj_org_article_82bc433180a3454296e12eb40f3f71c4
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_doaj_primary_oai_doaj_org_article_82bc433180a3454296e12eb40f3f71c4
Other Identifiers
ISSN
1758-2946
E-ISSN
1758-2946
DOI
10.1186/s13321-021-00539-7
