Modelling and predicting the biological effects of nanomaterials

SAR and QSAR in Environmental Research

Volumn 25, Issue 2, 2014, Pages 161-172

  Winkler, D A ^a,b   Burden, F R ^a   Yan, B ^c   Weissleder, R ^d   Tassa, C ^d   Shaw, S ^d,e   Epa, V C ^a  

^a CSIRO   (Australia)

^b MONASH UNIVERSITY   (Australia)

^c Department of Applied Mathematics Shandong   (China)

^d MASSACHUSETTS GENERAL HOSPITAL   (United States)

^e BROAD INSTITUTE OF MIT AND HARVARD   (United States)

Author keywords

adverse biological effects; Bayesian methods; modelling and prediction; nanomaterials; neural network; QSAR

Indexed keywords

ASSAYS; BAYESIAN NETWORKS; CELL CULTURE; HEALTH RISKS; LEARNING SYSTEMS; MACROPHAGES; METAL NANOPARTICLES; MOLECULES; NEURAL NETWORKS;

ADVERSE BIOLOGICAL EFFECT; BAYESIAN METHODS; BIOLOGICAL EFFECTS; CELL LINES; COMMERCIAL APPLICATIONS; DATA SET; MACHINE LEARNING METHODS; MODELLING AND PREDICTIONS; NEURAL-NETWORKS; QSAR;

GOLD NANOPARTICLES;

NANOMATERIAL;

ANIMAL; ARTICLE; ARTIFICIAL NEURAL NETWORK; CELL LINE; CHEMISTRY; DRUG EFFECT; ENDOTHELIUM CELL; EPITHELIUM CELL; HUMAN; MACROPHAGE; QUANTITATIVE STRUCTURE ACTIVITY RELATION;

ANIMALS; CELL LINE; ENDOTHELIAL CELLS; EPITHELIAL CELLS; HUMANS; MACROPHAGES; NANOSTRUCTURES; NEURAL NETWORKS (COMPUTER); QUANTITATIVE STRUCTURE-ACTIVITY RELATIONSHIP;

EID: 84896316551     PISSN: 1062936X     EISSN: 1029046X     Source Type: Journal    
DOI: 10.1080/1062936X.2013.874367     Document Type: Article

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