Using natural language processing techniques to inform research on nanotechnology

Scholarly Works

• Gomes Souza, F.; Bhansali, S.; Pal, K.; Silveira Maranhão, F. d.; Santos Oliveira, M.; Valladão, V. S.; Brandão E Silva, D. S.; Silva, G. B. A 30-Year Review on Nanocomposites: Comprehensive Bibliometric Insights into Microstructural, Electrical, and Mechanical Properties Assisted by Artificial Intelligence. Materials (Basel, Switzerland) 2024, 17, 1088. doi:10.3390/ma17051088
• Balasamy, S.; Sekaran, S.; Eswaramoorthy, R. Supporting Nanotechnology Safety Through Nanoinformatics. Green Nanoremediation; Springer International Publishing, 2023; pp 313–332. doi:10.1007/978-3-031-30558-0_14
• Greenberg, Z. F.; Graim, K. S.; He, M. Towards artificial intelligence-enabled extracellular vesicle precision drug delivery. Advanced drug delivery reviews 2023, 199, 114974. doi:10.1016/j.addr.2023.114974
• Scott-Fordsmand, J. J.; Amorim, M. J. B. Using Machine Learning to make nanomaterials sustainable. The Science of the total environment 2022, 859, 160303. doi:10.1016/j.scitotenv.2022.160303
• Serov, N.; Vinogradov, V. Artificial intelligence to bring nanomedicine to life. Advanced drug delivery reviews 2022, 184, 114194. doi:10.1016/j.addr.2022.114194
• Weber, J. M.; Guo, Z.; Zhang, C.; Schweidtmann, A. M.; Lapkin, A. A. Chemical data intelligence for sustainable chemistry. Chemical Society reviews 2021, 50, 12013–12036. doi:10.1039/d1cs00477h
• Milanez, D. H.; de Faria, L. I. L.; Leiva, D. R. A text mining-based approach for the evaluation of patenting trends on nanomaterials. Journal of Nanoparticle Research 2021, 23, 1–12. doi:10.1007/s11051-021-05304-3
• Lavanya, B.; Sasipriya, G. Study on Emerging Machine Learning Trends on Nanoparticles—Nanoinformatics. Advances in Intelligent Systems and Computing; Springer Singapore, 2021; pp 443–458. doi:10.1007/978-981-16-2594-7_37
• Dasari, Y.; Kalluri, H. K.; Dondeti, V. A Crypto Scheme Using Data Obfuscation of Entity Detection and Replacement for Private Cloud. International Journal of Safety and Security Engineering 2020, 10, 417–422. doi:10.18280/ijsse.100315
• Afantitis, A.; Melagraki, G.; Isigonis, P.; Tsoumanis, A.; Varsou, D.-D.; Valsami-Jones, E.; Papadiamantis, A. G.; Ellis, L. J. A.; Sarimveis, H.; Doganis, P.; Karatzas, P.; Tsiros, P.; Liampa, I.; Lobaskin, V.; Greco, D.; Serra, A.; Kinaret, P. A. S.; Saarimäki, L. A.; Grafström, R. C.; Kohonen, P.; Nymark, P.; Willighagen, E.; Puzyn, T.; Rybińska-Fryca, A.; Lyubartsev, A. P.; Jensen, K. A.; Brandenburg, J. G.; Lofts, S.; Svendsen, C.; Harrison, S.; Maier, D.; Tämm, K.; Jänes, J.; Sikk, L.; Dusinska, M.; Longhin, E.; Rundén-Pran, E.; Mariussen, E.; Yamani, N. E.; Unger, W.; Radnik, J.; Tropsha, A.; Cohen, Y.; Leszczynski, J.; Hendren, C. O.; Wiesner, M. R.; Winkler, D. A.; Suzuki, N.; Yoon, T. H.; Choi, J.-S.; Sanabria, N.; Gulumian, M.; Lynch, I. NanoSolveIT Project: Driving nanoinformatics research to develop innovative and integrated tools for in silico nanosafety assessment. Computational and structural biotechnology journal 2020, 18, 583–602. doi:10.1016/j.csbj.2020.02.023
• Richarz, A.-N. CHAPTER 1 Big Data in Predictive Toxicology: Challenges, Opportunities and Perspectives. Big Data in Predictive Toxicology; The Royal Society of Chemistry, 2019; pp 1–37. doi:10.1039/9781782623656-00001
• Sason, H.; Shamay, Y. Nanoinformatics in Drug Delivery. Israel Journal of Chemistry 2019, 60, 1108–1117. doi:10.1002/ijch.201900042
• Casola, G.; Siegmund, C.; Mattern, M.; Sugiyama, H. Data mining algorithm for pre-processing biopharmaceutical drug product manufacturing records. Computers & Chemical Engineering 2019, 124, 253–269. doi:10.1016/j.compchemeng.2018.12.001
• Li, Y.-P.; Han, K.; Grambow, C. A.; Green, W. H. Self-Evolving Machine: A Continuously Improving Model for Molecular Thermochemistry. The journal of physical chemistry. A 2019, 123, 2142–2152. doi:10.1021/acs.jpca.8b10789
• Basei, G.; Hristozov, D.; Lamon, L.; Zabeo, A.; Jeliazkova, N.; Tsiliki, G.; Marcomini, A.; Torsello, A. Making use of available and emerging data to predict the hazards of engineered nanomaterials by means of in silico tools: A critical review. NanoImpact 2019, 13, 76–99. doi:10.1016/j.impact.2019.01.003
• Jensen, C. D.; Lewinski, N. Nanoparticle synthesis to green informatics frameworks. Current Opinion in Green and Sustainable Chemistry 2018, 12, 117–126. doi:10.1016/j.cogsc.2018.08.005
• Saini, B.; Srivastava, S. Nanotoxicity prediction using computational modelling - review and future directions. IOP Conference Series: Materials Science and Engineering 2018, 348, 012005. doi:10.1088/1757-899x/348/1/012005
• Kim, E.; Huang, K. J.-M.; Saunders, A.; McCallum, A.; Ceder, G.; Olivetti, E. Materials Synthesis Insights from Scientific Literature via Text Extraction and Machine Learning. Chemistry of Materials 2017, 29, 9436–9444. doi:10.1021/acs.chemmater.7b03500
• Lewinski, N.; Jimenez, I.; McInnes, B. T. An annotated corpus with nanomedicine and pharmacokinetic parameters. International journal of nanomedicine 2017, 12, 7519–7527. doi:10.2147/ijn.s137117
• Tchoua, R.; Chard, K.; Audus, D.; Ward, L.; Lequieu, J.; de Pablo, J. J.; Foster, I. eScience - Towards a Hybrid Human-Computer Scientific Information Extraction Pipeline. In 2017 IEEE 13th International Conference on e-Science (e-Science), IEEE, 2017; pp 109–118. doi:10.1109/escience.2017.23

Explore citations to this article at