Single-Cell Genomics | Biomedical AI | Cancer Bioinformatics | Scientific Software

I am a biotechnology graduate and computational biology researcher interested in using bioinformatics, omics data, and machine learning to investigate complex biological and disease processes.

My current work focuses on single-cell transcriptomics, cancer bioinformatics, biomedical AI, and reproducible computational analysis, with growing interests in neurogenomics and quantitative biology.

• Single-cell genomics
• Cancer bioinformatics
• Biomedical AI
• Neurogenomics
• Gene regulation
• Computational & quantitative biology

An interactive scientific platform for in-silico digestion, peptide-to-species mapping, ZooMS marker exploration, and paleoproteomics analysis.

Tools: Python · R · Shiny · Bioinformatics

An end-to-end deep-learning workflow for lesion segmentation, localization, and benign–malignant classification from breast ultrasound images.

Methods: CNNs · ResNet · EfficientNet · DenseNet · Vision Transformers

Reproducible workflows for analyzing single-cell RNA-seq datasets, including quality control, normalization, dimensionality reduction, clustering, integration, and biological interpretation.

Tools: R · Seurat v5 · SCTransform · RPCA

Bioinformatics investigation of genetic and post-transcriptional regulation of FOXA1 in breast cancer, including SNP and miRNA analyses.

Programming: R · Python

Single-Cell & Omics: Seurat v5 · SCTransform · RPCA · RNA-seq · scRNA-seq · Bioconductor · GEO · TCGA

Machine Learning: CNNs · ResNet · EfficientNet · DenseNet · Vision Transformers

Scientific Computing: Jupyter Notebook · R Shiny · Git/GitHub · Data Visualization

I am currently developing reproducible computational approaches for single-cell genomics and exploring how machine learning can help characterize disease-associated cellular states and biological heterogeneity.

LinkedIn: Hagar El-Azab
Email: hagarhaitham227@gmail.com