Nano-Lazar: Read across Predictions for Nanoparticle Toxicities with Calculated and Measured Properties
The lazar framework for read across predictions was expanded for the prediction of nanoparticle toxicities, and a new methodology for calculating nanoparticle descriptors from core and coating structures was implemented. Nano-lazar provides a flexible and reproducible framework for downloading data...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2017-06-01
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| Series: | Frontiers in Pharmacology |
| Subjects: | |
| Online Access: | http://journal.frontiersin.org/article/10.3389/fphar.2017.00377/full |
| _version_ | 1811265585159340032 |
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| author | Christoph Helma Micha Rautenberg Denis Gebele |
| author_facet | Christoph Helma Micha Rautenberg Denis Gebele |
| author_sort | Christoph Helma |
| collection | DOAJ |
| description | The lazar framework for read across predictions was expanded for the prediction of nanoparticle toxicities, and a new methodology for calculating nanoparticle descriptors from core and coating structures was implemented. Nano-lazar provides a flexible and reproducible framework for downloading data and ontologies from the open eNanoMapper infrastructure, developing and validating nanoparticle read across models, open-source code and a free graphical interface for nanoparticle read-across predictions. In this study we compare different nanoparticle descriptor sets and local regression algorithms. Sixty independent crossvalidation experiments were performed for the Net Cell Association endpoint of the Protein Corona dataset. The best RMSE and r2 results originated from models with protein corona descriptors and the weighted random forest algorithm, but their 95% prediction interval is significantly less accurate than for models with simpler descriptor sets (measured and calculated nanoparticle properties). The most accurate prediction intervals were obtained with measured nanoparticle properties (no statistical significant difference (p < 0.05) of RMSE and r2 values compared to protein corona descriptors). Calculated descriptors are interesting for cheap and fast high-throughput screening purposes. RMSE and prediction intervals of random forest models are comparable to protein corona models, but r2 values are significantly lower. |
| first_indexed | 2024-04-12T20:26:29Z |
| format | Article |
| id | doaj.art-daf1301151b848e591a7f9c09fc744a1 |
| institution | Directory Open Access Journal |
| issn | 1663-9812 |
| language | English |
| last_indexed | 2024-04-12T20:26:29Z |
| publishDate | 2017-06-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Pharmacology |
| spelling | doaj.art-daf1301151b848e591a7f9c09fc744a12022-12-22T03:17:52ZengFrontiers Media S.A.Frontiers in Pharmacology1663-98122017-06-01810.3389/fphar.2017.00377252214Nano-Lazar: Read across Predictions for Nanoparticle Toxicities with Calculated and Measured PropertiesChristoph HelmaMicha RautenbergDenis GebeleThe lazar framework for read across predictions was expanded for the prediction of nanoparticle toxicities, and a new methodology for calculating nanoparticle descriptors from core and coating structures was implemented. Nano-lazar provides a flexible and reproducible framework for downloading data and ontologies from the open eNanoMapper infrastructure, developing and validating nanoparticle read across models, open-source code and a free graphical interface for nanoparticle read-across predictions. In this study we compare different nanoparticle descriptor sets and local regression algorithms. Sixty independent crossvalidation experiments were performed for the Net Cell Association endpoint of the Protein Corona dataset. The best RMSE and r2 results originated from models with protein corona descriptors and the weighted random forest algorithm, but their 95% prediction interval is significantly less accurate than for models with simpler descriptor sets (measured and calculated nanoparticle properties). The most accurate prediction intervals were obtained with measured nanoparticle properties (no statistical significant difference (p < 0.05) of RMSE and r2 values compared to protein corona descriptors). Calculated descriptors are interesting for cheap and fast high-throughput screening purposes. RMSE and prediction intervals of random forest models are comparable to protein corona models, but r2 values are significantly lower.http://journal.frontiersin.org/article/10.3389/fphar.2017.00377/fullnanoparticletoxicityQSARread-acrosspredictive toxicologymachine learning |
| spellingShingle | Christoph Helma Micha Rautenberg Denis Gebele Nano-Lazar: Read across Predictions for Nanoparticle Toxicities with Calculated and Measured Properties Frontiers in Pharmacology nanoparticle toxicity QSAR read-across predictive toxicology machine learning |
| title | Nano-Lazar: Read across Predictions for Nanoparticle Toxicities with Calculated and Measured Properties |
| title_full | Nano-Lazar: Read across Predictions for Nanoparticle Toxicities with Calculated and Measured Properties |
| title_fullStr | Nano-Lazar: Read across Predictions for Nanoparticle Toxicities with Calculated and Measured Properties |
| title_full_unstemmed | Nano-Lazar: Read across Predictions for Nanoparticle Toxicities with Calculated and Measured Properties |
| title_short | Nano-Lazar: Read across Predictions for Nanoparticle Toxicities with Calculated and Measured Properties |
| title_sort | nano lazar read across predictions for nanoparticle toxicities with calculated and measured properties |
| topic | nanoparticle toxicity QSAR read-across predictive toxicology machine learning |
| url | http://journal.frontiersin.org/article/10.3389/fphar.2017.00377/full |
| work_keys_str_mv | AT christophhelma nanolazarreadacrosspredictionsfornanoparticletoxicitieswithcalculatedandmeasuredproperties AT micharautenberg nanolazarreadacrosspredictionsfornanoparticletoxicitieswithcalculatedandmeasuredproperties AT denisgebele nanolazarreadacrosspredictionsfornanoparticletoxicitieswithcalculatedandmeasuredproperties |