Home
I’m Amir Aynede, a Master’s student in Bioinformatics at the Department of Pharmacy and Biotechnology (FaBiT), Alma Mater Studiorum – University of Bologna, Italy.
My academic background and research interests lie at the intersection of biology and computation, with a particular focus on:
- Computational Biology and Structural Bioinformatics
- Cancer Clinical Data Bioinformatics
- Machine Learning Applications in Diseases
My work integrates data science, biological insights, and modern AI to explore complex biomedical problems, especially in the context of cancer research.
You can explore a collection of my recent and ongoing projects on my GitHub page, each accompanied by detailed documentation to ensure full reproducibility — including code, notebooks, reports, and automated workflows.
One of my most recent and comprehensive works is a machine learning pipeline for cancer classification using histopathological images from the LC25000 dataset. The goal of this project was to automate cancer subtype classification using deep learning — addressing limitations of manual histopathology such as subjectivity and variability across human observers.
The pipeline encompasses all stages of a modern AI project: data preprocessing, training a fine-tuned ResNet18 model, model evaluation using metrics like accuracy, precision, recall, and F1-score, and interpretability using Grad-CAM to visualize decision-relevant regions of images.
This project reflects both the technical depth and the scientific purpose that guide my work in biomedical informatics.