Results 231 to 240 of about 169,533 (318)
A machine learning method, opt‐GPRNN, is presented that combines the advantages of neural networks and kernel regressions. It is based on additive GPR in optimized redundant coordinates and allows building a representation of the target with a small number of terms while avoiding overfitting when the number of terms is larger than optimal.
Sergei Manzhos, Manabu Ihara
wiley +1 more source
Exploring significant gender disparities in esophageal cancer risk: Insights from Mendelian randomization analysis and global burden of disease data. [PDF]
Cui J +7 more
europepmc +1 more source
TLS 1.3 for engineers: An exploration of the TLS 1.3 specification and Oracle's Java implementation [PDF]
Ben Smyth
openalex
Predictive models successfully screen nanoparticles for toxicity and cellular uptake. Yet, complex biological dynamics and sparse, nonstandardized data limit their accuracy. The field urgently needs integrated artificial intelligence/machine learning, systems biology, and open‐access data protocols to bridge the gap between materials science and safe ...
Mariya L. Ivanova +4 more
wiley +1 more source
CCL20 secreted by KRT15high tumor Cells promotes tertiary lymphoid structure formation and enhances anti-PD-1 therapy response in HPV+HNSCC. [PDF]
Zhang S +12 more
europepmc +1 more source
Quantitative phase maps of single cells recorded in flow cytometry modality feed a hierarchical architecture of machine learning models for the label‐free identification of subtypes of ovarian cancer. The employment of a priori clinical information improves the classification performance, thus emulating the clinical application of liquid biopsy during ...
Daniele Pirone +11 more
wiley +1 more source
Single-cell spatial analysis identifies ID1-high endothelial cells in tertiary lymphoid structures as predictors of durable response to immunotherapy in non-small cell lung cancer. [PDF]
Matsumoto K +23 more
europepmc +1 more source
A Hybrid Transfer Learning Framework for Brain Tumor Diagnosis
A novel hybrid transfer learning approach for brain tumor classification achieves 99.47% accuracy using magnetic resonance imaging (MRI) images. By combining image preprocessing, ensemble deep learning, and explainable artificial intelligence (XAI) techniques like gradient‐weighted class activation mapping and SHapley Additive exPlanations (SHAP), the ...
Sadia Islam Tonni +11 more
wiley +1 more source

