We present a comparison of machine learning methods for the prediction of four quantitative traits in Arabidopsis thaliana. High prediction accuracies were achieved on individuals grown under standardized laboratory conditions from the 1001 Arabidops...
OBJECTIVE: Despite the prevalent use of the general linear model (GLM) in fMRI data analysis, assuming a pre-defined hemodynamic response function (HRF) for all voxels can lead to reduced reliability and may distort the inferences derived from it. To...
The random parameters Generalized Linear Model (GLM) is frequently used to model speeding characteristics and capture the heterogenous effects of factors. However, this statistical approach is seldom employed for prediction and generalization due to ...
Soil heavy metal pollution poses a serious threat to food security, human health, and soil ecosystems. Based on 644 soil samples collected from a typical oasis located at the eastern margin of the Tarim Basin, a series of models, namely, multiple lin...
Journal of clinical monitoring and computing
39162839
Artificial neural networks (ANNs) are versatile tools capable of learning without prior knowledge. This study aims to evaluate whether ANN can calculate minute volume during spontaneous breathing after being trained using data from an animal model of...
Rapid urbanization and industrialization have intensified air pollution, posing severe health risks and necessitating accurate PM predictions for effective urban air quality management. This study distinguishes itself by utilizing high-resolution ERA...
Journal of the American Academy of Orthopaedic Surgeons. Global research & reviews
39106479
BACKGROUND: Accurate and precise templating is paramount for anatomic total shoulder arthroplasty (TSA) and reverse total shoulder arthroplasty (RSA) to enhance preoperative planning, streamline surgery, and improve implant positioning. We aimed to e...
OBJECTIVES: To evaluate the performance of an artificial intelligence (AI) model in predicting orthognathic surgical outcomes compared to conventional prediction methods.
This study was designed to predict the post-weaning weights of Akkaraman lambs reared on different farms using multiple linear regression and machine learning algorithms. The effect of factors the age of the dam, gender, type of lambing, enterprise, ...
Journal of the American Medical Informatics Association : JAMIA
39225790
OBJECTIVES: The retinal age gap (RAG) is emerging as a potential biomarker for various diseases of the human body, yet its utility depends on machine learning models capable of accurately predicting biological retinal age from fundus images. However,...