Frontiers

Artificial Intelligence in Cancer Genetics: Deciphering Gene Regulatory Networks and Transcriptional Dynamics

Cancer tumorigenesis is fundamentally driven by the profound dysregulation of gene regulatory networks (GRNs) and complex epigenetic alterations. With the ...
IEEE

Optimizing Machine Learning-Based Ovarian Cancer Prediction Through Normalization Strategies

Abstract: Ovarian cancer is one of the most challenging cancers to detect early, often leading to poor survival rates. This study explores supervised and unsupervised machine learning and deep ...
IEEE

A Comparative Study of Machine Learning Models for Breast Cancer Diagnosis Using the Wisconsin Diagnostic Dataset

Abstract: Accurate and interpretable diagnostic models are vital for early detection of breast cancer. This paper presents a comparative analysis of five classifiers Logistic Regression (LogReg), ...
Frontiers

An explainable machine learning model predicts pediatric varicella encephalitis

Pediatric varicella encephalitis is a rare but serious complication of varicella, which has a significant impact on patient prognosis. Early clinical diagnosis is still challenging due to atypical ...