Ibrahim Alsaggaf

This paper introduces GAN-RCL, a contrastive learning method for cell-type identification in single-cell RNA-Seq that outperforms existing methods.

This study evaluates contrastive learning generative networks for estimating cell-type distributions in single-cell RNA-Seq data, finding supervised methods outperform self-supervised ones.

The paper introduces Gen-DNA-TCN, a method for generating functional yeast promoter sequences using deep learning, and shows it produces diverse and functional sequences with similar transcription factor binding sites.

The paper evaluates and optimizes sequence-based gene regulatory deep learning models using a DREAM Challenge dataset, showing improvements through architectural and training strategy refinements.

This paper introduces a contrastive learning framework with Gaussian noise augmentation for improved cell-type prediction in scRNA-seq data.