An automated curation procedure for addressing chemical errors and inconsistencies in public datasets used in QSAR modelling
about
sameAs
High-throughput dietary exposure predictions for chemical migrants from food contact substances for use in chemical prioritization.History of EPI Suite™ and future perspectives on chemical property estimation in US Toxic Substances Control Act new chemical risk assessments.The CompTox Chemistry Dashboard: a community data resource for environmental chemistry.Pharmaceuticals in a temperate forest-water reuse system.How good are publicly available web services that predict bioactivity profiles for drug repurposing?Expert judgment based multicriteria decision models to assess the risk of pesticides on reproduction failures of grey partridge.Suspect screening and non-targeted analysis of drinking water using point-of-use filters.Demonstration of a consensus approach for the calculation of physicochemical properties required for environmental fate assessments.Predicting in vivo effect levels for repeat-dose systemic toxicity using chemical, biological, kinetic and study covariates.In silico toxicology protocols.A comparison of three liquid chromatography (LC) retention time prediction models.OPERA models for predicting physicochemical properties and environmental fate endpoints.A statistical approach to identify, monitor, and manage incomplete curated data sets.“MS-Ready” structures for non-targeted high-resolution mass spectrometry screening studiesDiagnostics of Data-Driven Models: Uncertainty Quantification of PM7 Semi-Empirical Quantum Chemical Method
P2860
Q38596889-B0692B73-5CA4-4A0A-927C-50FB28D774A7Q38748527-2551A7C4-7F45-406B-8704-68D918A2185CQ44040193-0D35210B-E554-4680-A353-07E51A0A0F8AQ44869366-2FC21568-5B1A-43A5-A646-6B9075FD4B7BQ45943515-186D6BE5-A742-4216-9EEB-66E50043477EQ47366662-C8B3D973-BE6A-44A8-A0C2-BB175A501BD0Q47402380-63FD31DD-69A1-47CF-8CB6-5529510C5476Q47590087-AD9E8D0D-297D-4571-B11A-75667E41CEAEQ50093723-1AB8D621-D54B-4B9F-A0DE-2367CFC79BE4Q52573546-846408E1-D825-4579-91FE-6462A21778D8Q53080779-B21369F4-C2B4-424B-9D7C-DD6289D6E349Q54974457-F06E30DF-9C8D-4F0F-97AC-C5297049F618Q55070315-AAC1197A-196C-4253-9D1F-98BE6AEA9A58Q56392431-1B543810-1216-42A5-B75E-6EA75A7B7A1BQ58763500-5F87F063-C496-496B-B1C9-7473B3564816
P2860
An automated curation procedure for addressing chemical errors and inconsistencies in public datasets used in QSAR modelling
description
2016 nî lūn-bûn
@nan
2016 թուականի Նոյեմբերին հրատարակուած գիտական յօդուած
@hyw
2016 թվականի նոյեմբերին հրատարակված գիտական հոդված
@hy
2016年の論文
@ja
2016年論文
@yue
2016年論文
@zh-hant
2016年論文
@zh-hk
2016年論文
@zh-mo
2016年論文
@zh-tw
2016年论文
@wuu
name
An automated curation procedur ...... atasets used in QSAR modelling
@ast
An automated curation procedur ...... atasets used in QSAR modelling
@en
type
label
An automated curation procedur ...... atasets used in QSAR modelling
@ast
An automated curation procedur ...... atasets used in QSAR modelling
@en
altLabel
An automated curation procedur ...... tasets used in QSAR modelling$
@en
An automated curation procedur ...... tasets used in QSAR modelling.
@en
prefLabel
An automated curation procedur ...... atasets used in QSAR modelling
@ast
An automated curation procedur ...... atasets used in QSAR modelling
@en
P50
P921
P1476
An automated curation procedur ...... atasets used in QSAR modelling
@en
P2093
A M Richard
R S Judson
P304
P356
10.1080/1062936X.2016.1253611
P577
2016-11-01T00:00:00Z