An ISA-TAB-Nano based data collection framework to support data-driven modelling of nanotoxicology

Scholarly Works

• Ammar, A.; Evelo, C.; Willighagen, E. FAIR assessment of nanosafety data reusability with community standards. Scientific data 2024, 11, 503. doi:10.1038/s41597-024-03324-x
• Mortensen, H. M.; Beach, B.; Slaughter, W.; Senn, J.; Williams, A.; Boyes, W. Translating nanoEHS data using EPA NaKnowBase and the resource description framework. F1000Research 2024, 13, 169. doi:10.12688/f1000research.141056.1
• Toropova, A. P.; Toropov, A. A. The CORAL Software as a Tool to Develop Models for Nanomaterials' Endpoints. Challenges and Advances in Computational Chemistry and Physics; Springer International Publishing, 2023; pp 351–371. doi:10.1007/978-3-031-28401-4_14
• Jeliazkova, N.; Kochev, N.; Tancheva, G. FAIR Data Model for Chemical Substances: Development Challenges, Management Strategies, and Applications. Data Integrity and Data Governance; IntechOpen, 2023. doi:10.5772/intechopen.110248
• Furxhi, I. Health and environmental safety of nanomaterials: O Data, Where Art Thou?. NanoImpact 2021, 25, 100378. doi:10.1016/j.impact.2021.100378
• Bossa, C.; Andreoli, C.; Bakker, M.; Barone, F.; De Angelis, I.; Jeliazkova, N.; Nymark, P.; Battistelli, C. L. FAIRification of nanosafety data to improve applicability of (Q)SAR approaches: A case study on in vitro Comet assay genotoxicity data. Computational toxicology (Amsterdam, Netherlands) 2021, 20, 100190. doi:10.1016/j.comtox.2021.100190
• Yadav, R. D.; Chaudhary, A. Nano-bio surface interactions, cellular internalisation in cancer cells and e-data portals of nanomaterials: A review. IET nanobiotechnology 2021, 15, 519–531. doi:10.1049/nbt2.12040
• Shin, H. K.; Kim, S.; Yoon, S. Use of size-dependent electron configuration fingerprint to develop general prediction models for nanomaterials. NanoImpact 2021, 21, 100298. doi:10.1016/j.impact.2021.100298
• Sohail, M. I.; Ayub, M. A.; Rehman, M. Z. u.; Azhar, M.; Farooqi, Z. U. R.; Siddiqui, A.; Umar, W.; Iftikhar, I.; Nadeem, M.; Fatima, H. Sufficiency and toxicity limits of metallic oxide nanoparticles in the biosphere. Nanomaterials: Synthesis, Characterization, Hazards and Safety; Elsevier, 2021; pp 145–221. doi:10.1016/b978-0-12-823823-3.00002-1
• Ayadi, A.; Rose, J.; de Garidel-Thoron, C.; Hendren, C. O.; Wiesner, M. R.; Auffan, M. MESOCOSM: A mesocosm database management system for environmental nanosafety. NanoImpact 2020, 21, 100288. doi:10.1016/j.impact.2020.100288
• Martinez, D. S. T.; da Silva, G. H.; de Medeiros, A. M. Z.; Khan, L. U.; Papadiamantis, A. G.; Lynch, I. Effect of the Albumin Corona on the Toxicity of Combined Graphene Oxide and Cadmium to Daphnia magna and Integration of the Datasets into the NanoCommons Knowledge Base. Nanomaterials (Basel, Switzerland) 2020, 10, 1936. doi:10.3390/nano10101936
• Kochev, N.; Jeliazkova, N.; Paskaleva, V.; Tancheva, G.; Iliev, L.; Ritchie, P.; Jeliazkov, V. Your Spreadsheets Can Be FAIR: A Tool and FAIRification Workflow for the eNanoMapper Database. Nanomaterials (Basel, Switzerland) 2020, 10, 1908. doi:10.3390/nano10101908
• Furxhi, I.; Murphy, F.; Mullins, M.; Arvanitis, A.; Poland, C. A. Nanotoxicology data for in silico tools: a literature review. Nanotoxicology 2020, 14, 612–637. doi:10.1080/17435390.2020.1729439
• Escorihuela, L.; Martorell, B.; Rallo, R.; Fernández, A. Toward computational and experimental characterisation for risk assessment of metal oxide nanoparticles. Environmental Science: Nano 2018, 5, 2241–2251. doi:10.1039/c8en00389k
• Golbamaki, A.; Golbamaki, N.; Sizochenko, N.; Rasulev, B.; Leszczynski, J.; Benfenati, E. Genotoxicity induced by metal oxide nanoparticles: a weight of evidence study and effect of particle surface and electronic properties. Nanotoxicology 2018, 12, 1113–1129. doi:10.1080/17435390.2018.1478999
• Karcher, S. C.; Willighagen, E.; Rumble, J.; Ehrhart, F.; Evelo, C. T.; Fritts, M.; Gaheen, S.; Harper, S. L.; Hoover, M. D.; Jeliazkova, N.; Lewinski, N.; Robinson, R. L. M.; Mills, K.; Mustad, A. P.; Thomas, D. G.; Tsiliki, G.; Hendren, C. O. Integration among databases and data sets to support productive nanotechnology: Challenges and recommendations. NanoImpact 2018, 9, 85–101. doi:10.1016/j.impact.2017.11.002
• Scott-Fordsmand, J. J.; Amorim, M. J.; Sørensen, P. Implementing the DF4 in a robust model, allowing for enhanced comparison, prioritisation and grouping of Nanomaterials. Regulatory toxicology and pharmacology : RTP 2017, 92, 207–212. doi:10.1016/j.yrtph.2017.12.008
• Puzyn, T.; Jeliazkova, N.; Sarimveis, H.; Robinson, R. L. M.; Lobaskin, V.; Rallo, R.; Richarz, A.-N.; Gajewicz, A.; Papadopulos, M. G.; Hastings, J.; Cronin, M. T. D.; Benfenati, E.; Fernández, A. Perspectives from the NanoSafety Modelling Cluster on the validation criteria for (Q)SAR models used in nanotechnology. Food and chemical toxicology : an international journal published for the British Industrial Biological Research Association 2017, 112, 478–494. doi:10.1016/j.fct.2017.09.037
• Brehm, M.; Kafka, A.; Bamler, M.; Kühne, R.; Schüürmann, G.; Sikk, L.; Burk, J.; Burk, P.; Tamm, T.; Tämm, K.; Pokhrel, S.; Mädler, L.; Kahru, A.; Aruoja, V.; Sihtmäe, M.; Scott-Fordsmand, J. J.; Sørensen, P.; Escorihuela, L.; Roca, C. P.; Fernández, A.; Giralt, F.; Rallo, R. An Integrated Data-Driven Strategy for Safe-by-Design Nanoparticles: The FP7 MODERN Project. Advances in experimental medicine and biology 2017, 947, 257–301. doi:10.1007/978-3-319-47754-1_9
• Richarz, A.-N.; Avramopoulos, A.; Benfenati, E.; Gajewicz, A.; Bakhtyari, N. G.; Leonis, G.; Robinson, R. L. M.; Papadopoulos, M. G.; Cronin, M. T. D.; Puzyn, T. Compilation of Data and Modelling of Nanoparticle Interactions and Toxicity in the NanoPUZZLES Project. Advances in experimental medicine and biology 2017, 947, 303–324. doi:10.1007/978-3-319-47754-1_10

Explore citations to this article at