Fallahi Bioinformatix Lab@2021-2025

Making sense of Bio/Med big data

🧬 About my lab

The Fallahi Bioinformatics Lab at Razi University is a growing research group focused on applying computational biology and machine learning to better understand biological aspects of aging, cancer, and cellular senescence. Led by Dr. Hossein Fallahi, Associate Professor with a background in molecular genetics and bioinformatics, the lab integrates genomic and transcriptomic data to investigate disease mechanisms and potential therapeutic strategies. We combine established bioinformatics tools with emerging AI techniques to analyze large-scale biological datasets, aiming to identify meaningful patterns and generate new hypotheses for experimental validation. Our current work includes exploring gene expression changes in aging tissues, senescence-associated markers in cancer, and multi-omics approaches for precision medicine. Recent publications in journals such as Biogerontology, Frontiers in Molecular Neuroscience, and PLOS ONE reflect our commitment to collaborative, data-driven research. We maintain active partnerships across disciplines, including neuroscience and cancer biology, and are particularly interested in bridging computational models with practical applications in healthcare. The lab also supports graduate training in bioinformatics and computational biology, assisting students in building the analytical skills required for their academic success.

If you’re working in related areas, looking to collaborate, or just curious about what we do, feel free to get in touch. We’d be happy to connect.

chat with us See the documentation

Drawing logo of bioinformatics in the lab

Highlights

Our Research

We have implemented multiple Bioinformatics techniques in cancer and stem cell biology to uncover important pathways and genes in these fields.

See what we’ve published  →

Our Resources

Applications and codes that have been used or developed in our group would be a good starting point for many who want to conduct similar analyses. We will deposit them in the resource section and update them regularly.

See our resources  →

Our Team

please visit our GitHub page to look at the codes that we are using. We have modified available codes to better integrate them into our analysis pipelines.

Here are the team members  →