Dilbar Urazbaeva

Predictive modeling of oral squamous cell carcinoma risk using a multi-parametric machine learning framework integrating clinical, sociodemographic, and salivary biomarker data

Oral Squamous Cell Carcinoma (OSCC) represents a major public health challenge due to diagnostic delays and the invasive nature of conventional biopsy techniques, underscoring the need for accurate, non-invasive prognostic tools. This study develops and validates a multi-parametric machine learning framework that int...

Neuro-Immune Interactions in the Tumour Microenvironment: Implications for Cancer Progression and Treatment.

The nervous and immune systems, once viewed as distinct biological networks, are now understood to be closely linked in regulating cancer development, progression, and responses to therapy. Neural structures infiltrate the tumour microenvironment and communicate with immune cells, influencing key processes such as tumo...

A Privacy‐Preserving and Explainable Deep Learning Framework for Imbalanced Student Performance Prediction Using CTGAN‐Based Data Augmentation

A privacy‐preserving and explainable deep learning framework that integrates Conditional Tabular Generative Adversarial Network (CTGAN)‐based augmentation, adaptive model selection, and gradient‐based interpretability for small‐scale tabular data is proposed.

Therapeutic potential of apigenin in neurodegenerative diseases and brain health