Azam Sheikh Muhammad

This paper introduces ExpressionGAN, a deep learning model that generates functional regulatory DNA with specified expression levels, outperforming random mutagenesis approaches.

The paper uses machine learning and differential expression analysis to identify secretory pathway genes associated with cancer characteristics, finding KIF20A and KIF23 as key genes across multiple cancer types.

The paper uses deep learning to show that gene expression levels are encoded in both coding and non-coding regions of the gene regulatory structure across multiple organisms.