biomarkers

This paper presents a granularity-aware EEG feature framework that organizes multi-scale descriptors into global, regional, and channel levels to predict dimensional psychopathology. Using the HBN cohort, it shows that tree-based models and granularity-balanced feature selection yield modest improvements, suggesting multi-scale EEG features contain weak but detectable signals for pediatric mental health.

This study develops an XGBoost classifier using SHAP explainability on eight clinical biomarkers from the ADNI dataset to achieve three-class Alzheimer's disease detection (normal cognition, MCI, AD), reaching a macro AUC of 0.982 and Cohen's kappa of 0.909 on the held-out test set. SHAP analysis identifies CDR Global as the dominant predictor for NC and MCI, while CDR-SB and MMSE together drive AD classification.

This study evaluates machine learning models for pre-test risk stratification of Chlamydia trachomatis infection using non-invasive patient-reported data and urine biomarkers, demonstrating moderate predictive performance and the complementary value of both data types.

This paper proposes a residual gap-aware transformer that combines a mixed-effects statistical reference with transformer-based residual learning to forecast 24-month CDR-SB change from ADNI clinical and biomarker histories, achieving reduced MSE and improved correlation over baselines.

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