Predicting and investigating cytotoxicity of nanoparticles by translucent machine learning

Designing for a green chemistry future

(2020) Julie B. Zimmerman et al.   SCIENCE

Machine learning predicts the functional composition of the protein corona and the cellular recognition of nanoparticles

(2020) Zhan Ban et al.   PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA

Construction of a web-based nanomaterial database by big data curation and modeling friendly nanostructure annotations

(2020) Xiliang Yan et al.   Nature Communications

The cytotoxicity of nanomaterials: Modeling multiple human cells uptake of functionalized magneto-fluorescent nanoparticles via nano-QSAR

(2020) Ronghua Qi et al.   CHEMOSPHERE

Visualizing the effects of predictor variables in black box supervised learning models

(2020) Daniel W. Apley et al.   JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY

Predicting Cytotoxicity of Metal Oxide Nanoparticles Using Isalos Analytics Platform

(2020) Anastasios G. Papadiamantis et al.   Nanomaterials

Predicting nanotoxicity by an integrated machine learning and metabolomics approach

(2020) Ting Peng et al.   ENVIRONMENTAL POLLUTION

Risk assessment of heterogeneous TiO2-based engineered nanoparticles (NPs): a QSTR approach using simple periodic table based descriptors

(2019) Joyita Roy et al.   Nanotoxicology

Supervised Learning and Mass Spectrometry Predicts the in Vivo Fate of Nanomaterials

(2019) James Lazarovits et al.   ACS Nano

Bayesian Network Resource for Meta-Analysis: Cellular Toxicity of Quantum Dots

(2019) Muhammad Bilal et al.   Small

Recent advances and applications of machine learning in solid-state materials science

(2019) Jonathan Schmidt et al.   npj Computational Materials

LightGBM: An Effective and Scalable Algorithm for Prediction of Chemical Toxicity–Application to the Tox21 and Mutagenicity Data Sets

(2019) Jin Zhang et al.   Journal of Chemical Information and Modeling

Definitions, methods, and applications in interpretable machine learning

(2019) W. James Murdoch et al.   PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA

Zinc oxide nanoparticles induce toxicity in CAL 27 oral cancer cell lines by activating PINK1/Parkin-mediated mitophagy

(2018) Jianfeng Wang et al.   International Journal of Nanomedicine

Dependence of Nanoparticle Toxicity on Their Physical and Chemical Properties

(2018) Alyona Sukhanova et al.   Nanoscale Research Letters

Genotoxicity induced by metal oxide nanoparticles: a weight of evidence study and effect of particle surface and electronic properties

(2018) Azadi Golbamaki et al.   Nanotoxicology

A framework for sustainable nanomaterial selection and design based on performance, hazard, and economic considerations

(2018) Mark M. Falinski et al.   Nature Nanotechnology

Advanced tools for the safety assessment of nanomaterials

(2018) Bengt Fadeel et al.   Nature Nanotechnology

Dependence of Nanoparticle Toxicity on Their Physical and Chemical Properties

(2018) Alyona Sukhanova et al.   Nanoscale Research Letters

The mythos of model interpretability

(2018) Zachary C. Lipton   COMMUNICATIONS OF THE ACM

Machine learning for molecular and materials science

(2018) Keith T. Butler et al.   NATURE

Quasi-QSAR for predicting the cell viability of human lung and skin cells exposed to different metal oxide nanomaterials

(2018) Jang-Sik Choi et al.   CHEMOSPHERE

Decision tree models to classify nanomaterials according to the DF4nanoGrouping scheme

(2017) Agnieszka Gajewicz et al.   Nanotoxicology

Reproducibility, sharing and progress in nanomaterial databases

(2017) Alexander Tropsha et al.   Nature Nanotechnology

Predicting toxic potencies of metal oxide nanoparticles by means of nano-QSARs

(2016) Yunsong Mu et al.   Nanotoxicology

Meta-analysis of cellular toxicity for cadmium-containing quantum dots

(2016) Eunkeu Oh et al.   Nature Nanotechnology

Peeking Inside the Black Box: Visualizing Statistical Learning With Plots of Individual Conditional Expectation

(2015) Alex Goldstein et al.   JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS

Cellular uptake, elimination and toxicity of CdSe/ZnS quantum dots in HepG2 cells

(2013) Lu Peng et al.   BIOMATERIALS

Gradient boosting machines, a tutorial

(2013) Alexey Natekin et al.   Frontiers in Neurorobotics

Nanotoxicity assessment of quantum dots: from cellular to primate studies

(2012) Ken-Tye Yong et al.   CHEMICAL SOCIETY REVIEWS

The cytotoxicity of cadmium-based quantum dots

(2011) Nan Chen et al.   BIOMATERIALS

Some aspects of quantum dot toxicity

(2011) Melanie Bottrill et al.   CHEMICAL COMMUNICATIONS

Understanding and controlling the interaction of nanomaterials with proteins in a physiological environment

(2011) Carl D. Walkey et al.   CHEMICAL SOCIETY REVIEWS

Using nano-QSAR to predict the cytotoxicity of metal oxide nanoparticles

(2011) Tomasz Puzyn et al.   Nature Nanotechnology

Cytotoxicity screening of 23 engineered nanomaterials using a test matrix of ten cell lines and three different assays

(2011) Alexandra Kroll et al.   Particle and Fibre Toxicology

Cadmium-Free CuInS2/ZnS Quantum Dots for Sentinel Lymph Node Imaging with Reduced Toxicity

(2010) Thomas Pons et al.   ACS Nano

The cytotoxicity of cadmium based, aqueous phase – Synthesized, quantum dots and its modulation by surface coating

(2008) Yuanyuan Su et al.   BIOMATERIALS

Current in vitro methods in nanoparticle risk assessment: Limitations and challenges

(2008) Alexandra Kroll et al.   EUROPEAN JOURNAL OF PHARMACEUTICS AND BIOPHARMACEUTICS

Limitations and relative utility of screening assays to assess engineered nanoparticle toxicity in a human cell line

(2008) N.A. Monteiro-Riviere et al.   TOXICOLOGY AND APPLIED PHARMACOLOGY