Syed Ahsan Shahid

This paper develops a 14-protein plasma biomarker panel for prostate cancer diagnosis using machine learning, achieving high accuracy and outperforming existing methods.

This study uses machine learning to identify minimal gene signatures predicting antibiotic resistance in Pseudomonas aeruginosa with high accuracy.

The paper identifies a four-gene panel using machine learning that captures redox, proliferative, and immune pathway dysregulation in breast cancer, achieving high accuracy in tumor-healthy discrimination.

This paper develops a machine learning framework to predict antimicrobial resistance using transcriptomics data, emphasizing the need for high-quality datasets and feature reduction methods.