Genome-wide association studies (GWAS) have catalogued hundreds of thousands of genetic variants linked to complex human traits and diseases, with more than 625,000 variant-trait associations across ...
Abstract: High-dimensional gene expression data pose substantial challenges for machine-learning–based diagnostic modelling due to extreme dimensionality, noise, heterogeneous measurement conditions, ...
Identifying lithology is crucial for geological exploration, and the adoption of artificial intelligence is progressively becoming a refined approach to automate this process. A key feature of this ...
ABSTRACT: Cross entropy is a measure in machine learning and deep learning that assesses the difference between predicted and actual probability distributions. In this study, we propose cross entropy ...
With the increase in artificial intelligence in real-world applications, there is interest in building hybrid systems that take both human and machine predictions into account. Previous work has shown ...
External Validation of the Bone Metastases Ensemble Trees for Survival (BMETS) Machine Learning Model to Predict Survival in Patients With Symptomatic Bone Metastases Patient-level data from the ...
Abstract We address the estimation of the effective reproductive number R t based on serological data using Bayesian inference. We also explore the Bayesian learning paradigm to estimate R t . We ...
Abstract: K-dependence Bayesian network classifier(KDB) has been widely used in data mining and machine learning. To enhance the expression ability and classification performance of KDB, the present ...
Bayesian Networks are graphical models useful for various applications, including time series prediction and anomaly detection. Bayesian inference offers a robust set of tools for modelling ...
Some results have been hidden because they may be inaccessible to you
Show inaccessible results