Big data in chemical toxicity research: the use of high-throughput screening assays to identify potential toxicants
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ADMET evaluation in drug discovery: 15. Accurate prediction of rat oral acute toxicity using relevance vector machine and consensus modelingMechanism Profiling of Hepatotoxicity Caused by Oxidative Stress Using Antioxidant Response Element Reporter Gene Assay Models and Big DataPubChem BioAssay: 2017 updateEstimating Potency in High-Throughput Screening Experiments by Maximizing the Rate of Change in Weighted Shannon EntropyPredictive Modeling of Estrogen Receptor Binding Agents Using Advanced Cheminformatics Tools and Massive Public DataApplication of the TGx-28.65 transcriptomic biomarker to classify genotoxic and non-genotoxic chemicals in human TK6 cells in the presence of rat liver S9Supporting read-across using biological dataScreening Chemicals for Receptor-Mediated Toxicological and Pharmacological Endpoints: Using Public Data to Build Screening Tools within a KNIME Workflow.Developing Enhanced Blood-Brain Barrier Permeability Models: Integrating External Bio-Assay Data in QSAR ModelingCIIPro: a new read-across portal to fill data gaps using public large-scale chemical and biological data.Getting the most out of PubChem for virtual screeningBehavioral screening of the LOPAC1280 library in zebrafish embryos.Experimental Errors in QSAR Modeling Sets: What We Can Do and What We Cannot Do.The Next Era: Deep Learning in Pharmaceutical Research.Alternative approaches for identifying acute systemic toxicity: Moving from research to regulatory testing.The CompTox Chemistry Dashboard: a community data resource for environmental chemistry.Comparison of Deep Learning With Multiple Machine Learning Methods and Metrics Using Diverse Drug Discovery Data Sets.Evaluation of oxidative stress biomarkers in Aiolopus thalassinus (Orthoptera: Acrididae) collected from areas polluted by the fertilizer industry.Robust Microplate-Based Methods for Culturing and in Vivo Phenotypic Screening of Chlamydomonas reinhardtii.PubChem BioAssay: A Decade's Development toward Open High-Throughput Screening Data SharingBig-data and machine learning to revamp computational toxicology and its use in risk assessmentMachine Learning Based Toxicity Prediction: From Chemical Structural Description to Transcriptome Analysis
P2860
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P2860
Big data in chemical toxicity research: the use of high-throughput screening assays to identify potential toxicants
description
2014 nî lūn-bûn
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2014 թուականի Հոկտեմբերին հրատարակուած գիտական յօդուած
@hyw
2014 թվականի հոտեմբերին հրատարակված գիտական հոդված
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2014年の論文
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2014年学术文章
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2014年学术文章
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2014年学术文章
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2014年学术文章
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2014年学术文章
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2014年學術文章
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name
Big data in chemical toxicity ...... o identify potential toxicants
@ast
Big data in chemical toxicity ...... o identify potential toxicants
@en
Big data in chemical toxicity ...... o identify potential toxicants
@nl
type
label
Big data in chemical toxicity ...... o identify potential toxicants
@ast
Big data in chemical toxicity ...... o identify potential toxicants
@en
Big data in chemical toxicity ...... o identify potential toxicants
@nl
prefLabel
Big data in chemical toxicity ...... o identify potential toxicants
@ast
Big data in chemical toxicity ...... o identify potential toxicants
@en
Big data in chemical toxicity ...... o identify potential toxicants
@nl
P2093
P2860
P921
P3181
P356
P1476
Big data in chemical toxicity ...... o identify potential toxicants
@en
P2093
Abena Boison
Alexander Sedykh
Kimberlee Moran
Marlene T Kim
P2860
P304
P3181
P356
10.1021/TX500145H
P407
P577
2014-10-20T00:00:00Z