Machine learning enhances proteomics by optimizing peptide identification, structure prediction, and biomarker discovery.

Support vector machines improve classification by mapping inseparable signals into higher-dimensional spaces. Random forest models, through ensemble decision trees, increase robustness against ...

A Hybrid Machine Learning Framework for Early Diabetes Prediction in Sierra Leone Using Feature Selection and Soft-Voting Ensemble ...

Multimodal sensing in physical AI (PAI), sometimes called embodied AI, is the ability for AI to fuse diverse sensory inputs, ...

A dual-model battery health assessment framework analyzes real-world voltage data from retired EV batteries in grid storage. Using incremental ...

High-throughput sequencing, single-cell technologies, and large-scale population studies have transformed genetics and genomics into data-intensive ...

Background Patients with heart failure (HF) frequently suffer from undetected declines in cardiorespiratory fitness (CRF), which significantly increases their risk of poor outcomes. However, current ...

Machine learning can predict many things, but can it predict who will develop schizophrenia years before the average diagnosis time?

Machine learning models are usually complimented for their intelligence. However, their success mostly hinges on one fundamental aspect: data labeling for machine learning. A model has to get familiar ...

This important study describes long-range serial dependence of performance on a visual texture discrimination training task that manipulated conditions to induce differing degrees of location transfer ...

A research team from The Hong Kong University of Science and Technology (HKUST) has developed GrainBot, an AI-enabled toolkit ...

Background Gut microbiota dysbiosis is linked to autism spectrum disorder (ASD) in children. However, the role of bacterial ...