Machine learning enhances proteomics by optimizing peptide identification, structure prediction, and biomarker discovery.
Both approaches identified hemoglobin as one of the most significant predictors of CKD risk. Additional top-ranked features included blood urea, sodium levels, red blood cell count, potassium, and ...
Support vector machines improve classification by mapping inseparable signals into higher-dimensional spaces. Random forest models, through ensemble decision trees, increase robustness against ...
Read more about AI-driven air quality system promises faster, more reliable urban health warnings on Devdiscourse ...
“Tooth agenesis, a congenital condition characterized by the absence of one or more teeth, is among the most common and ...
A Hybrid Machine Learning Framework for Early Diabetes Prediction in Sierra Leone Using Feature Selection and Soft-Voting Ensemble ...
Time series electrocardiography combined with AI predicted cardiac arrest with remarkable accuracy. Discover how this ...
Objective Cardiovascular diseases (CVD) remain the leading cause of mortality globally, necessitating early risk ...
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 ...
The other is to invest in high-fidelity digital twins which are deemed to be powerful but costly, data-hungry, and often slow to implement. A practical middle ground is emerging: the disruption-aware ...
Human MAP1LC3B (LC3B) binds proteins involved in autophagy and other cellular processes using a degenerate four-residue short linear motif known as the LC3-interacting region (LIR). Biochemical and ...
Background Gut microbiota dysbiosis is linked to autism spectrum disorder (ASD) in children. However, the role of bacterial ...
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