Identification of structural features of surface modifiers in engineered nanostructured metal oxides regarding cell uptake through ML-based classification

• Indrasis Dasgupta,
• Totan Das,
• Biplab Das and
• Shovanlal Gayen

Beilstein J. Nanotechnol. 2024, 15, 909–924, doi:10.3762/bjnano.15.75

• -based ML models, namely, random forest classifier (RFC), support vector classifier (SVC), linear discriminant analysis (LDA), and logistic regression (LR) were developed in the current analysis. These models were developed using the optimized hyper parameters in the Scikit Learn package. The ML models

• the highest performance for the PaCa2 cell line, while the support vector classifier (SVC) model demonstrated superior performance for the HUVEC cell line. The linear discriminant analysis (LDA) model performed best for the U937 cell line. Figure 6A–F depicts the ROC curves for the compounds in the

• discriminant analysis (LDA), yielded fivefold cross-validated ROC values of 0.735 for the training set and 0.630 for the test set. The findings revealed distinctive structural fingerprints associated with the cellular uptake of nanoparticles in each cell line (Figure 10). For example, the presence of a hydroxy

On the additive artificial intelligence-based discovery of nanoparticle neurodegenerative disease drug delivery systems

• Shan He,
• Julen Segura Abarrategi,
• Harbil Bediaga,
• Sonia Arrasate and
• Humberto González-Díaz

Beilstein J. Nanotechnol. 2024, 15, 535–555, doi:10.3762/bjnano.15.47

• comprising 4403 NDD assays from ChEMBL and 260 NP cytotoxicity assays from journal articles. Through a resampling process, three new working datasets were generated, each containing 500,000 cases. We utilized linear discriminant analysis (LDA) along with artificial neural network (ANN) algorithms, such as

• serve as valuable tools in the design of drug delivery systems for neurosciences. Keywords: artificial neural network (ANN); linear discriminant analysis (LDA); machine learning; nanoparticle; neurodegenerative diseases; Introduction Over time, there has been a significant shift in global dietary

• classification of NDD and NP information. The generic equation for the IFPTML linear model is the following (Equation 4): Generalities for IFPTML model training and validation series In many big data systems, the linear discriminant analysis (LDA) model is the most commonly used tool to seek the preliminary

A chemiresistive sensor array based on polyaniline nanocomposites and machine learning classification

• Jiri Kroutil,
• Alexandr Laposa,
• Ali Ahmad,
• Jan Voves,
• Vojtech Povolny,
• Ladislav Klimsa,
• Marina Davydova and
• Miroslav Husak

Beilstein J. Nanotechnol. 2022, 13, 411–423, doi:10.3762/bjnano.13.34

• studies have been implemented in recent years using different statistical analysis algorithms, like principal component analysis (PCA), linear discriminant analysis (LDA), k-nearest neighbors algorithm (KNN), support vector machine (SVM), decision tree classifier (DT), random forest (RF), and Gaussian

Plasmonics-based detection of H₂ and CO: discrimination between reducing gases facilitated by material control

• Gnanaprakash Dharmalingam,
• Nicholas A. Joy,
• Benjamin Grisafe and
• Michael A. Carpenter

Beilstein J. Nanotechnol. 2012, 3, 712–721, doi:10.3762/bjnano.3.81

• sensing experiments using both supervised and unsupervised statistical algorithms, linear-discriminant analysis (LDA) and principal-component analysis (PCA), respectively [22]. This study has practical benefits in that relevant wavelength regions can be identified from the entire plasmon spectrum, as