Artificial augmented dataset for the enhancement of nano-QSARs models. A methodology based on topological projections

Author:

Furxhi Irini12ORCID,Kalapus Michal3ORCID,Costa Anna4ORCID,Puzyn Tomasz35ORCID

Affiliation:

1. Dept. of Accounting and Finance, Kemmy Business School, University of Limerick, Ireland

2. Transgero Limited, Cullinagh, Newcastle West, Co. Limerick, Limerick, Ireland

3. Laboratory of Environmental Chemoinformatics, Department of Environmental Chemistry and Radiochemistry, Faculty of Chemistry, University of Gdansk, Gdansk, Poland

4. CNR-ISSMC Istituto di Scienza, Tecnologia e Sostenibilità per lo Sviluppo dei Materiali Ceramici, Faenza, Italy

5. QSAR Lab Ltd, Gdansk, Poland

Funder

European Union’s Horizon

research

National Science Center, Poland

Narodowe Centrum Nauki

Publisher

Informa UK Limited

Subject

Toxicology,Biomedical Engineering

Reference57 articles.

1. Data augmentation in natural language processing: a novel text generation approach for long and short text classifiers

2. Dimensionality reduction for visualizing single-cell data using UMAP

3. Parallel Poisson disk sampling with spectrum analysis on surfaces

4. Bowles C et al. 2018. “Gan augmentation: Augmenting training data using generative adversarial networks.”

5. Bridson, R. 2007. “Fast Poisson Disk Sampling in Arbitrary Dimensions.” ACM SIGGRAPH 2007 Sketches. San Diego, California, Association for Computing Machinery: 22–es. https://doi.org/10.1145/1278780.1278807

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1. Correction;Nanotoxicology;2023-08-09

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