Supporting Information
| Supporting Information File 1: The dataset used to develop the five individual models. The NMs used in training and test sets are also indicated. | ||
| Format: CSV | Size: 10.1 KB | Download |
| Supporting Information File 2: Details of the kNN/read-across model presented following the QMRF format. | ||
| Format: PDF | Size: 369.4 KB | Download |
| Supporting Information File 3: Details of the random forest model presented following the QMRF format. | ||
| Format: PDF | Size: 283.6 KB | Download |
| Supporting Information File 4: Details of the AdaBoost regression model presented following the QMRF format. | ||
| Format: PDF | Size: 363.9 KB | Download |
| Supporting Information File 5: Details of the stacked PLS and MLP q-RASPR models presented following the QMRF format. | ||
| Format: PDF | Size: 412.9 KB | Download |
Cite the Following Article
The round-robin approach applied to nanoinformatics: consensus prediction of nanomaterials zeta potential
Dimitra-Danai Varsou, Arkaprava Banerjee, Joyita Roy, Kunal Roy, Giannis Savvas, Haralambos Sarimveis, Ewelina Wyrzykowska, Mateusz Balicki, Tomasz Puzyn, Georgia Melagraki, Iseult Lynch and Antreas Afantitis
Beilstein J. Nanotechnol. 2024, 15, 1536–1553.
https://doi.org/10.3762/bjnano.15.121
How to Cite
Varsou, D.-D.; Banerjee, A.; Roy, J.; Roy, K.; Savvas, G.; Sarimveis, H.; Wyrzykowska, E.; Balicki, M.; Puzyn, T.; Melagraki, G.; Lynch, I.; Afantitis, A. Beilstein J. Nanotechnol. 2024, 15, 1536–1553. doi:10.3762/bjnano.15.121
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Presentation Graphic
| Picture with graphical abstract, title and authors for social media postings and presentations. | ||
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