Results 161 to 170 of about 47,079 (313)
Ovarian Cancer Diagnosis and Chemoresistance Prediction Model Based on cfRNA Molecular Signature
A deep learning model analyzes cfRNA profiles extracted from the blood of OVCA patients. This innovative approach distinguishes OVCA from healthy controls with high accuracy. Crucially, it reliably predicts patient response to chemotherapy (sensitive versus resistant subgroups).
Qinhao Guo +14 more
wiley +1 more source
A precise model for skin cancer diagnosis using hybrid U-Net and improved MobileNet-V3 with hyperparameters optimization. [PDF]
Kumar Lilhore U +8 more
europepmc +1 more source
Prediction of Sepsis Using LSTM with Hyperparameter Optimization with a Genetic Algorithm
Petr Nejedlý +4 more
openalex +1 more source
Hyperopt: a Python library for model selection and hyperparameter optimization [PDF]
James Bergstra +4 more
openalex +1 more source
Exact Discrete Stochastic Simulation With Deep‐Learning‐Scale Gradient Optimization
A 203,796‐parameter gene regulatory network classifies handwritten digits with 98.4% accuracy using exact stochastic dynamics. The framework decouples forward simulation from backward differentiation, making continuous‐time Markov chain models compatible with deep‐learning optimization.
Jose M. G. Vilar, Leonor Saiz
wiley +1 more source
A Dynamic Early Stopping Criterion for Random Search in SVM Hyperparameter Optimization
Adrian Cătălin Florea, Răzvan Andonie
openalex +2 more sources
Machine‐Learning Framework for Designing Stable Interfaces in All‐Solid‐State Lithium‐Ion Batteries
A data‐driven strategy is developed to discover coating materials for all‐solid‐state lithium batteries. Using calculations of interfacial reactivity, unsupervised pattern recognition, and machine‐learning prediction, the study identifies low‐reactivity compositional patterns and screens new lithium‐based oxide and polyanion candidates, extending ...
Sehyeok Park +4 more
wiley +1 more source
Large‐scale UK Biobank analyses identify clinical and proteomic signatures for early prediction of valvular heart disease and its subtypes. Proteins add predictive value for VHD, AVS, and MVR, with outcome‐specific compact panels showing translational potential. Multi‐layer evidence highlights matrix remodeling, protease regulation, immune inflammation,
Zhihao Jiang +10 more
wiley +1 more source

