On the public citation graph

Benchmark Performance of MultiCASE Inc. Software in Ames Mutagenicity Set

https://doi.org/10.1021/ci1000899
0 signed · 17 inferred · view as graph

Inferred edges come from two machine sources: sweeps of open repository metadata (mostly SSRN and Zenodo), and a bulk OpenAlex citation snapshot. Every row below is labelled with the one it came from. Neither is a complete citation index — absence here is not evidence of absence.

Is this your work? Sign in with ORCID and put your name on the claims that are yours.
Claim with ORCID
Reference health. See whether every reference in this work resolves to a known work and none has been retracted — and get a dated CiteStamped badge you can embed.
See reference health

What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1021/ci900161gopenalex-snapshot
Benchmark Data Set for in Silico Prediction of Ames Mutagenicity

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.1002/9783527801756.ch20openalex-snapshot
Early Drug Development
inferred← citeshttps://doi.org/10.1002/minf.201200081openalex-snapshot
Effectiveness of CASE Ultra Expert System in Evaluating Adverse Effects of Drugs
inferred← citeshttps://doi.org/10.1007/978-1-0716-1960-5_19openalex-snapshot
MultiCASE Platform for In Silico Toxicology
inferred← citeshttps://doi.org/10.1007/978-3-642-25240-2_55openalex-snapshot
In Silico Methods
inferred← citeshttps://doi.org/10.1016/b978-0-12-396461-8.00008-7openalex-snapshot
Computational Translation and Integration of Test Data to Meet Risk Assessment Goals
inferred← citeshttps://doi.org/10.1016/b978-0-443-36474-7.00036-3openalex-snapshot
Computational modeling of acute and chronic toxicities of chemicals of emerging concern
inferred← citeshttps://doi.org/10.1016/j.tiv.2017.02.016openalex-snapshot
Novel naïve Bayes classification models for predicting the chemical Ames mutagenicity
inferred← citeshttps://doi.org/10.1016/j.vascn.2013.12.003openalex-snapshot
Progress in computational toxicology
inferred← citeshttps://doi.org/10.1016/j.yrtph.2012.01.007openalex-snapshot
In silico methods combined with expert knowledge rule out mutagenic potential of pharmaceutical impurities: An industry survey
inferred← citeshttps://doi.org/10.1016/j.yrtph.2013.05.001openalex-snapshot
Use of in silico systems and expert knowledge for structure-based assessment of potentially mutagenic impurities
inferred← citeshttps://doi.org/10.1016/j.yrtph.2013.08.008openalex-snapshot
Validation of Toxtree and SciQSAR in silico predictive software using a publicly available benchmark mutagenicity database and their applicability for the qualification of impurities in pharmaceuticals
inferred← citeshttps://doi.org/10.1021/ci300111ropenalex-snapshot
Optimizing Predictive Performance of CASE Ultra Expert System Models Using the Applicability Domains of Individual Toxicity Alerts
inferred← citeshttps://doi.org/10.1021/ci500016vopenalex-snapshot
Merging Applicability Domains for in Silico Assessment of Chemical Mutagenicity
inferred← citeshttps://doi.org/10.1039/c5tx00055fopenalex-snapshot
Computational toxicology, friend or foe?
inferred← citeshttps://doi.org/10.1517/17425255.2012.724059openalex-snapshot
Latest advances in computational genotoxicity prediction
inferred← citeshttps://doi.org/10.2174/1568026618666180727152557openalex-snapshot
Applications of Machine Learning Methods in Drug Toxicity Prediction
Editorial review for independent work. ICSAC — the Institute for Complexity Science and Advanced Computing — runs a structured editorial panel for researchers publishing outside journals, and publishes on CiteStamp rails. Submission is free and goes through ORCID: icsacinstitute.org/submit.