WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection
Permanent URI for this collectionhttps://hdl.handle.net/11147/7150
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Article Citation - WoS: 5Citation - Scopus: 4Identifying Factors Controlling Cellular Uptake of Gold Nanoparticles by Machine Learning(TAYLOR & FRANCIS LTD, 2023) Bilgi, Eyüp; Bilgi, Eyüp; Öksel Karakuş, Ceyda; 03.01. Department of Bioengineering; 03. Faculty of Engineering; 01. Izmir Institute of TechnologyThere is strong interest to improve the therapeutic potential of gold nanoparticles (GNPs) while ensuring their safe development. The utility of GNPs in medicine requires a molecular-level understanding of how GNPs interact with biological systems. Despite considerable research efforts devoted to monitoring the internalisation of GNPs, there is still insufficient understanding of the factors responsible for the variability in GNP uptake in different cell types. Data-driven models are useful for identifying the sources of this variability. Here, we trained multiple machine learning models on 2077 data points for 193 individual nanoparticles from 59 independent studies to predict cellular uptake level of GNPs and compared different algorithms for their efficacies of prediction. The five ensemble learners (Xgboost, random forest, bootstrap aggregation, gradient boosting, light gradient boosting machine) made the best predictions of GNP uptake, accounting for 80-90% of the variance in the test data. The models identified particle size, zeta potential, GNP concentration and exposure duration as the most important drivers of cellular uptake. We expect this proof-of-concept study will foster the more effective use of accumulated cellular uptake data for GNPs and minimise any methodological bias in individual studies that may lead to under- or over-estimation of cellular internalisation rates.Article Citation - WoS: 9Citation - Scopus: 11Prediction of Vinegar Processing Parameters With Chemometric Modelling of Spectroscopic Data(Elsevier, 2021) Çavdaroğlu, Çağrı; Özen, Fatma Banu; Özen, Banu; Özen, Fatma Banu; 03.08. Department of Food Engineering; 01. Izmir Institute of Technology; 03. Faculty of EngineeringSpectroscopic methods have the advantages of being rapid and environmentally friendly and can be used in measurement and control of processing parameters during food production. It was aimed to predict several quality and chemical parameters of vinegar processing from UV-visible and mid-infrared spectroscopic profiles. Two processing lines of both traditional and submerged vinegar production from 2 separate grape varieties (green and red grapes) were monitored. Some of the important markers of the fermentation processes; pH, brix, total acidity, total flavonoid content, total and individual phenolic contents, organic acid, sugar, ethanol concentrations as well as UV-visible and mid-infrared spectra were obtained during both types of vinegar processing and quality and chemical parameters were predicted from spectroscopic data using chemometric methods. Individual UV-visible and mid-infrared spectral profiles along with low level of data fusion were used in building of chemometric prediction models. Accurate, reliable and robust prediction models (R(2)cal and R(2)val >0.9) were obtained for quality parameters mostly with combination of two spectroscopic datasets. Predictive models used for phenolic components were below average except for p-coumaric and syringic acids. Citric and acetic acids were the most accurately estimated ones among organic acids along with ethanol. Close agreements between reference and predicted values were obtained during the monitoring of changes of some quality parameters for vinegar fermentation process through rapid and simultaneous spectroscopic measurements.